The University of Glasgow is seeking applications for the following funded PhD position in the area of Explainable AI, Planning, and Human-Machine Interaction. Interested students should apply as soon as possible

Eligibility: UK/EU students only

Start date: 1 January 2019

Full details at https://www.findaphd.com/search/ProjectDetails.aspx?PJID=98605  

Many future industrial operations will be carried out by teams consisting of humans and machines. In this project, the student will investigate how human-machine trust and explainable/transparent artificial intelligence affect such human-machine collaborative tasks. The work will concentrate on the communication aspects: how the machine communicates its intentions and reasoning processes to the human, and how the human can query and interact with the robot’s plan.

The project will be driven by oilfield drilling applications, which involve control of complex equipment in a dynamic environment, with an increasing level of automation. In this setting, close coordination and trust between the human crew and the automation system is required: the crew must both understand why the machine acts the way it does, as well as be confident it has taken all available information into account.

The student should have excellent experience, enthusiasm and skills in the areas of artificial intelligence and/or automated planning and reasoning and/or natural language or multimodal interaction. Applicants must hold a good Bachelor’s or Master’s degree in a relevant discipline.

The project is an EPSRC iCASE award with Schlumberger Gould Research and it is expected that the student will spend some time working with the company in Cambridge. This will give you a great opportunity of working in an internationally excellent research group as well as a leading player in the oil and gas industry. Travel and accommodation costs will be paid during the time at Schlumberger.

For more information, please contact Dr Mary Ellen Foster MaryEllen.Foster@glasgow.ac.uk. Feel free to contact me directly if you have questions, but only applications received through the University of Glasgow (as linked from the FindAPHD page) can be considered officially.

Back

Top

UNIVERSITY OF CALIFORNIA, SANTA CRUZ DEPARTMENT OF Computer Science and Engineering Assistant and Associate or Full Professor, Natural Language Processing 

 The Department of Computer Science and Engineering at the University of California, Santa Cruz invites applications for two positions in the field of Natural Language Processing. One position is at the tenured Associate or early stage Full Professor level, and the other position is at the tenure track Assistant Professor level. We seek outstanding applicants with research and teaching expertise in all areas of Natural Language Processing. We are especially interested in candidates who have contributed to one or more application areas of Natural Language Processing including but not limited to information extraction, dialogue systems, semantic parsing, sentiment analysis, question answering, and machine translation.  
 Both positions are associated with a proposed Professional MS program in Natural Language Processing to be located in the UCSC Silicon Valley Campus in Santa Clara, California. The successful candidates will play an essential role in developing, growing, and shaping this new program. They are expected to develop a research program, advise Ph.D. students in their research area, obtain external funding, develop and teach courses within the undergraduate and graduate curriculum, perform university, public, and professional service, and interact broadly with the large number of Natural Language Processing practitioners in Silicon Valley industrial research and advanced development labs. The successful candidates should be able to work with students, faculty and staff from a wide range of social and cultural backgrounds. In addition to the basic qualifications, applicants at the Associate or Full Professor level should have a demonstrated record of publications, demonstrated experience in university teaching at the undergraduate and graduate level or closely analogous activities, demonstrated record of extramural funding or similar success with garnering support for research endeavors, experience with research project management, and professional service; we also value industrial experience, and a track record of building product and applications based on NLP technology.
 We welcome candidates who understand the barriers facing women and minorities who are underrepresented in higher education careers (as evidenced by life experiences and educational background), and who have experience in equity and diversity with respect to teaching, mentoring, research, life experiences, or service towards building an equitable and diverse scholarly environment.
 The primary offices for these positions are located in Santa Clara, due to the expectation of teaching and mentoring students in this location. Space for PhD students for these positions is also located in Santa Clara. Graduate level teaching duties will be mainly at the Santa Clara campus with undergraduate courses to be taught at the Santa Cruz campus.  The successful applicants will typically spend multiple days per week in Santa Clara and are also expected to spend on average one day per week on the Santa Cruz campus (more when teaching an undergraduate class on the Santa Cruz campus). The ability for ondemand transportation between Santa Clara and Santa Cruz with or without accommodations is essential.
 The Computer Science and Engineering Department has nationally and internationally known research groups in Machine Learning, Data Science, Natural Language Processing and related fields. Our beautiful campus has a long history of embracing groundbreaking interdisciplinary work, and the proximity of the campus to Silicon Valley affords our faculty extensive opportunities for interactions and collaborations with industry. 
 ACADEMIC TITLES Assistant Professor and Associate or early stage Full Professor
 SALARY Commensurate with qualifications and experience; academic year (9-month basis).
 BASIC QUALIFICATIONS A Ph.D. or equivalent foreign degree in Computer Science or a relevant field expected to be completed by June 30, 2019; demonstrated record of research and teaching.

 POSITION AVAILABLE July 1, 2019 (with academic year beginning September 2019). Degree must be in hand by June 30, 2019. 
 APPLICATION REQUIREMENTS Applications are accepted via the UCSC Academic Recruit online system; all documents and materials must be submitted as PDFs. 
 APPLY AT https://recruit.ucsc.edu/apply/JPF00657  Please refer to Position # JPF00657-19 in all correspondence. 
 Documents/Materials  • Letter of application that briefly summarizes your qualifications and interest in the position  • Curriculum vitae • Statement addressing contributions to diversity through research, teaching, and/or service (required). Guidelines on diversity statements can be viewed at https://senate.ucsc.edu/committees/caad-committee-on-affirmative-action-anddiversity/DivStateGuidelines.pdf. • Statement of research plans  • Statement of teaching interests and experience • 3–4 selected publications • 3 confidential letters of recommendation*
 Please note that your references, or dossier service, will submit their confidential letters directly to the UC Recruit System.
 *All letters will be treated as confidential per University of California policy and California state law. For any reference letter provided via a third party (i.e., dossier service, career center), direct the author to UCSC’s confidentiality statement at http://apo.ucsc.edu/confstm.htm.
 RECRUITMENT PERIOD Full consideration will be given to applications completed by December 3rd, 2018. Applications received after this date will be considered only if the position has not been filled.
 
UC Santa Cruz faculty make significant contributions to the body of research that has earned the University of California the ranking as the foremost public higher education institution in the world. In the process, our faculty demonstrate that cutting-edge research, excellent teaching and outstanding service are mutually supportive.
 The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status. UC Santa Cruz is committed to excellence through diversity and strives to establish a climate that welcomes, celebrates, and promotes respect for the contributions of all students and employees. Inquiries regarding the University’s equal employment opportunity policies may be directed to the Office for Diversity, Equity, and Inclusion at the University of California, Santa Cruz, CA 95064 or by phone at (831) 459-2686.
 Under Federal law, the University of California may employ only individuals who are legally able to work in the United States as established by providing documents as specified in the Immigration Reform and Control Act of 1986. Certain UCSC positions funded by federal contracts or sub-contracts require the selected candidate to pass an E-Verify check (see https://www.uscis.gov/e-verify). More information is available at the APO website (see https://apo.ucsc.edu/policy/capm/104.000%20.html) or call (831) 459-4300.
 UCSC is a smoke & tobacco-free campus.  
 If you need accommodation due to a disability, please contact the Academic Personnel Office at apo@ucsc.edu (831) 459-4300.  VISIT THE APO WEB SITE AT http://apo.ucsc.edu [10/2/2018]

Back

Top

PhD student ?Morphology in spoken word recognition models?

Location:       Radboud University, Nijmegen

Duration:       4 years

Starting date:  February 2019

Starting salary:   Around 1600 euros a month

Supervision:   
The PhD project will be supervised by Louis ten Bosch, Mirjam Ernestus, and Ingo Plag .
The project is part of the ?Spoken Morphology? research unit (http://www.spoken-morphology.hhu.de/en.html)

Requirements:   
We are looking for candidates with
?       Master degree in Linguistics or in Artificial Intelligence;
?       Clear interest in speech and language;
?       Expertise in qualitative and/or quantitative research methods, preferably including large scale data analyses and statistical analyses;
?       Effective verbal and written communicative skills in English.

Application:   
Please send your motivation letter and extensive resume (including grades for all university courses) before 1 December 2018  to m.ernestus@let.ru.nl

Questions:             
l.tenbosch@let.ru.nl or m.ernestus@let.ru.nl

Back

Top

# Intitulé

Comparaison de fonctions objectif pour l?apprentissage de représentation :
application à la vérification du locuteur et au calcul de similarité sémantique textuelle

# Résumé

Le rôle de la fonction objectif dans l?apprentissage neuronal est de fournir une mesure de la performance du réseau de neurones (i.e. sa capacité à répondre correctement à une tâche précise). Cette mesure, lorsqu?elle est dérivable, permet alors de mettre à jour le réseau de neurones par rétro-propagation du gradient de telle sorte que sa performance soit améliorée. Parmi ces fonctions objectif, on peut par exemple citer la ?contrastive loss? [HCL06], la ?triplet loss? [SKP15], ou encore la ?center loss? [WZLQ16]. L?objectif de ce stage est de comparer différentes fonctions objectif permettant l?apprentissage des représentations neuronales adaptées à des tâches applicatives telles que la vérification du locuteur et la similarité sémantique textuelle. La plupart de ces méthodes ont été initialement proposées dans le domaine de la vision par ordinateur pour la reconnaissance d?image (et de visage en particulier) et certaines ont !
 été appliquées récemment à tâche de vérification du locuteur [Bre17]. Cependant, elles n?ont pas encore été utilisées pour la tâche de similarité sémantique textuelle.

# Description des tâches

* Implémentation des différentes fonctions objectif
Après une étape d?étude de la littérature sur le sujet, la première tâche consiste à implémenter les fonctions objectif les plus prometteuses en les testant sur des exemples jouet bien maîtrisés (tels que la base MNIST de reconnaissance de chiffre manuscrit, par exemple).

* Application à la vérification du locuteur
La tâche de vérification du locuteur consiste à déterminer si deux signaux audio proviennent ou non de l?enregistrement du même locuteur. On utilisera la base de données VoxCeleb [CNZ18, NCZ17] pour mener ces expériences. Elle contient plus d?un million d?enregistrements correspondant à plus de 6000 locuteurs, et constitue de fait le plus grand corpus librement disponible pour l?identification et la vérification du locuteur.

* Application au calcul de similarité sémantique textuelle
La tâche de similarité sémantique textuelle (SST) est motivée par le fait que la modélisation de la similarité sémantique des phrases est un problème fondamental en compréhension de la langue, pertinent pour de nombreuses applications, notamment la traduction automatique, la recherche de réponses à des questions précises (ou questions-réponses), le dialogue dialogue, etc. Cette tâche consiste à évaluer dans quelle mesure deux phrases sont sémantiquement équivalentes. Plusieurs approches ont étés proposées [CDA + 17], qui sont fondées généralement soit sur les méthodes classiques en traitement automatique des langues (TAL), soit sur des méthodes d?apprentissage profond. La première approche s?appuie sur l?utilisation d?un classifieur enrichi par différents types de descripteurs : sémantiques, syntaxiques, etc. La deuxième est fondée sur l?exploitation des représentations de phrases et des architectures neuronales. Dans le cadre des ca!
 mpagnes d?évaluation SemEval, la tâche de SST a été proposée. Dans ce cadre, la tâche consiste pour le système de SST à attribuer un score de similarité à chaque paire de phrase sur une échelle de 0 (les deux phrases sont complètement différentes) à 5 (les deux phrases sont complè tement identiques)? Notre objectif dans ce stage est de pouvoir étudier les différentes fonctions objectif sur la tâche SST et de comparer nos résultats aux résultats obtenus par les différents systèmes ayant participé à la tâche 5 (en anglais) de la campagne d?évaluation SemEval 2017. Ce système fait la combinaison des approches de TAL et d?apprentissage profond.

# Profil attendu

* Master 2 en Informatique (ou e?quivalent), avec au moins une spe?cialite? en apprentissage, traitement automatique de la langue, et/ou traitement de la parole.
* Compe?tence techniques : python, linux

# Informations pratiques

* Dure?e du stage : 5-6 mois (stage pouvant donner lieu a? une poursuite en the?se)
* De?but du stage : date de de?but a? de?finir avec le stagiaire
* Gratification : environ 570? par mois. remboursement frais de transport et subvention cantine

# Références

[Bre17] Hervé Bredin. Tristounet : triplet loss for speaker turn embedding. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5430?5434. IEEE, 2017.

[CDA + 17] Daniel Cer, Mona Diab, Eneko Agirre, Inigo Lopez-Gazpio, and Lucia Specia. Semeval-2017 task 1 : Semantic textual similarity-multilingual and cross-lingual focused evaluation. arXiv preprint arXiv :1708.00055, 2017.

[CNZ18] Joon Son Chung, Arsha Nagr ni, and Andrew Zisserman. Voxceleb2 : Deep speaker recognition. arXiv preprint arXiv :1806.05622, 2018.

[HCL06] Raia Hadsell, Sumit Chopra, and Yann LeCun. Dimensionality reduction by learning an invariant mapping. In CVPR 2006, pages 1735?1742. IEEE, 2006.

[NCZ17] Arsha Nagrani, Joon Son Chung, and Andrew Zisserman. Voxceleb : a large-scale speaker identification dataset. arXiv preprint arXiv :1706.08612, 2017.

[SKP15] Florian Schroff, Dmitry Kalenichenko, and James Philbin. Facenet : A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 815?823, 2015.

[WZLQ16] Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao. A discriminative feature learning approach for deep face recognition. In European Conference on Computer Vision, pages 499?515. Springer, 2016.

Back

Top

Research engineer or post-doc position in Natural Language Processing: Introduction of semantic information in a speech recognition system

Supervisors: Irina Illina, MdC, Dominique Fohr, CR CNRS

Team: Multispeech, LORIA-INRIA (https://team.inria.fr/multispeech/)

Contact: illina@loria.fr, dominique.fohr@loria.fr

Duration: 12-15 months

Deadline to apply : December 20th, 2019

Required skills: Strong background in mathematics, machine learning (DNN), statistics, natural language processing and computer program skills (Perl, Python).

Following profiles are welcome, either:

• Strong background in signal processing

or

• Strong experience with natural language processing

Excellent English writing and speaking skills are required in any case. 

Candidates should email a detailed CV with diploma

LORIA is the French acronym for the ?Lorraine Research Laboratory in Computer Science and its Applications? and is a research unit (UMR 7503), common to CNRS, the University of Lorraine and INRIA. This unit was officially created in 1997. Loria?s missions mainly deal with fundamental and applied research in computer sciences.

MULTISPEECH is a joint research team between the Université of Lorraine, Inria, and CNRS. Its research focuses on speech processing, with particular emphasis to multisource (source separation, robust speech recognition), multilingual (computer assisted language learning), and multimodal aspects (audiovisual synthesis).

Context and objectives

Under noisy conditions, audio acquisition is one of the toughest challenges to have a successful automatic speech recognition (ASR). Much of the success relies on the ability to attenuate ambient noise in the signal and to take it into account in the acoustic model used by the ASR. Our DNN (Deep Neural Network) denoising system and our approach to exploiting uncertainties have shown their combined effectiveness against noisy speech.

 The ASR stage will be supplemented by a semantic analysis. Predictive representations using continuous vectors have been shown to capture the semantic characteristics of words and their context, and to overcome representations based on counting words. Semantic analysis will be performed by combining predictive representations using continuous vectors and uncertainty on denoising. This combination will be done by the rescoring component. All our models will be based on the powerful technologies of DNN.

The performances of the various modules will be evaluated on artificially noisy speech signals and on real noisy data. At the end, a demonstrator, integrating all the modules, will be set up.

Main activities

? study and implementation of a noisy speech enhancement module and a propagation of uncertainty module;

? design a semantic analysis module;

? design a module taking into account the semantic and uncertainty information.

References

 [Nathwani et al., 2018] Nathwani, K., Vincent, E., and Illina, I. DNN uncertainty propagation using GMM-derived uncertainty features for noise robust ASR, IEEE Signal Processing Letters, 2018.

[Nathwani et al., 2017] Nathwani, K., Vincent, E., and Illina, I. Consistent DNN uncertainty training and decoding for robust ASR, in Proc. IEEE Automatic Speech Recognition and Understanding Workshop, 2017.

[Nugraha et al., 2016] Nugraha, A., Liutkus, A., Vincent E. Multichannel audio source separation with deep neural networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2016.

 [Sheikh, 2016] Sheikh, I. Exploitation du contexte sémantique pour améliorer la reconnaissance des noms propres dans les documents audio diachroniques?, These de doctorat en Informatique, Université de Lorraine, 2016.

[Sheikh et al., 2016] Sheikh, I. Illina, I. Fohr, D. Linares, G. Learning word importance with the neural bag-of-words model, in Proc. ACL Representation Learning for NLP (Repl4NLP) Workshop, Aug 2016.

[Mikolov et al., 2013a] Mikolov, T. Chen, K., Corrado, G., and Dean, J. Efficient estimation of word representations in vector space, CoRR, vol. abs/1301.3781, 2013.

Back

Top

Associate Linguist [Français] job offer

Intitulé du poste :

Associate Linguist [French]

Champs linguistiques  :

Phonétique, Phonologie, Morphologie, Sémantique, Syntaxe, Lexicographie, TAL

Lieu :

Paris, France

Description du poste :

En tant qu’Associate Linguist, vous annoterez et réviserez des données linguistiques en français.  L’Associate Linguist contribuera également à un certain nombre de tâches en traitement automatique des langues, dont :

• Transcription phonétique/phonémique d’entrées lexicales
• Analyse de données acoustiques pour évaluer la synthèse vocale
• Annotation et révision de données linguistiques
• Labellisation de textes, désambiguisation, expansion, and normalisation des données
• Annotation d’entrées lexicales en respectant les codes de référence
• Evaluation des outputs système
• Dérivation de données en TAL
• Capacité à travailler de manière indépendante avec précision

Compétences requises:

• Locuteur de langue maternelle française, parfaite maîtrise de l’anglais
• Connaissance en transcriptions phonétiques et phonologiques
• Familiarité avec les techniques et outils de synthèse de la parole et de reconnaissance vocale
• Expérience en annotation
• Connaissances en phonétique, phonologie, sémantique, syntaxe, morphologie et lexicographie
• Excellentes compétences en communication orale et écrite
• Attention aux détails et compétences organisationnelles 

Compétences désirées :

• Diplôme en linguistique théorique et computationnelle et TAL
• Capacité à saisir rapidement les concepts techniques et les outils conçus en interne
• Vif intérêt pour la technologie et compétences en informatique
• Compétences en écoute de données orales
• Compétences en saisie de clavier rapide et précise
• Familiarité avec les logiciels de transcription
• Compétences en édition, correction grammaticale et orthographique
• Compétences en recherche

CV + lettre de motivation en Anglais : maroussia.houimli@adeccooutsourcing.fr

2730^E brut/mensuel + 50% Pass Navigo + Mutuelle

Maroussia HOUIMLI

Responsable recrutement

E maroussia.houimli@adeccooutsourcing.fr

Back

Top

Neural coreference resolution

Coreference resolution aims at detecting chains of coreference mentions in a text, that is mentions in the text that refer to the same entity.
While at first coreference resolution was split into two separated sub-problems, i.e. mention detections and resolution of coreferent mentions [1], thanks to the development of sophisticated neural models [2,3,4], end-to-end coreference resolution system can be based on a whole single model.
The aim of this stage is to study Sequence-to-Sequence [5] and Transformer [6] neural models for coreference resolution, integrating different types of attention mechanisms and possibly arbitrarily-long context [8], with the goal of understanding their impact in dealing with this complex NLP problem.

In this internship the student will implement parts of the systems for coreference resolution with Sequence-to-Sequence and Transformer neural models.
The student will run experiments on his own using GPUs, and the systems will be tested on the CoNLL Semeval 2012 benchmark [7].

Profile:
- Student for internship level stage (Master 2) in computer science, or from engineering school
- Computer science skills:
    Python programming with good knowledge of deep learning libraries (TensorFlow or PyTorch)
    Textual data manipulation (xml format, tabular format, CoNLL format)
- Interested in Natural Language Processing
- Skills in machine learning for probabilistic models

Context:

Back

Top

 Poste de post-doctorant à pouvoir au 14 janvier 2019 pour une durée de 6 mois (100%)
 La mission principale du postdoctorant sera d'établir une typologie des gestes dédiés à la correction phonétique. Il ou elle devra déterminer des invariants malgré une grande variabilité inter et intra enseignants.                       
 A l'issue du contrat, la personne recrutée devra livrer 10 gestes pertinents avec un descriptif à la fois acoustique et didactiques. Il devra ainsi fournir un dictionnaire des gestes correctifs avec des éléments de description a) des erreurs concernées par ces gestes (critères phonétiques), b) et de réalisation (critères de synchronisation avec la parole ; critères de réalisation gestuelle : éléments corporels impliqués : posture, visage, bras, mains, doigts + positions de départ et de fin du geste, dynamique)

Activités:
Il ou elle sera amené(e) à réaliser 2 tâches principales:                                                                                           
Tâche 1 : Etudier les paramètres caractéristiques des gestes et regrouper les gestes en famille
Tâche 2: Associer des gestes correctifs à un type d?erreur (segmental ou suprasegmental) ;
Pour ce faire, la personne recrutée sera amenée à annoter deux corpus existants [le corpus MULTIPHONIA (http://sldr.org/sldr000780/fr) et le corpus 'MVT', encore non référencé]. Il/elle devra identifier et décrire les paramètres pertinents liés la réalisation des gestes cibles en utilisant à la fois les critères de réalisation (éléments corporels concernés, positions départ et fin, dynamique) et les critères phonétiques (quel niveau/ erreur sont concernés par le geste).
L'enjeu sera d'annoter les paramètres gestuels du corpus de façon à servir de cahier des charges pour le développement d'un avatar dans la suite du projet.
Résultat attendu : listing des gestes relevés dans le corpus et proposition d'une typologie.

Compétences requises:
 -des connaissances approfondies en didactique des langues étrangères et en phonétique
- une maîtrise des techniques d'expérimentation et d'analyse, en particulier l'analyse de gestes
- une maîtrise des techniques de recueil et d'analyse des données
- une capacité à rendre compte et à présenter des résultats à l'oral
- de bonnes aptitudes rédactionnelles
- autonomie, sérieux et dynamisme

Environnement professionnel:
Le post-doc s'inscrit dans le cadre du projet INGPRO (INcidence des Gestes sur la PROnonciation) qui est un projet interdisciplinaire impliquant trois laboratoires toulousains (Octogone-Lordat, l?IRIT et le LAIRDIL) et l?entreprise Archean Technologies. Le/la post-doctorant(e)  sera encadré (e)  par Charlotte Alazard-Guiu, MCF au laboratoire Octogone-Lordat. Il / elle devra également travailler en collaboration avec les partenaires du projet et plus particulièrement en collaboration avec le stagiaire qui sera  recruté à l'IRIT en mars 2019 et qui travaillera sur la synchronisation geste/parole.

Charlotte Alazard-Guiu,
Maître de Conférences à l'Université de Toulouse II
DEFLE / UFR Langues (Bureau LA 393)
U.R.I Octogone-Lordat, EA4156 / Maison de la recherche (Bureau E. 1.15)
Mail: charlottealazard@gmail.com, alazard@univ-tlse2.fr
Tel: (+33) 0561502001 (DEFLE)
      (+33) 0561502471 (Octogone-Lordat)
Page web: http://octogone.univ-tlse2.fr/accueil/octogone-lordat/membres/alazard-guiu-charlotte-398503.kjsp?RH=1295596505355

Back

Top

The European Language resources Distribution Agency (ELDA), a company specialized in Human Language Technologies within an international context is currently seeking to fill an immediate vacancy for a Language Resources Project Manager ? Junior position. This yields excellent opportunities for young, creative, and motivated candidates wishing to participate actively to the Language Engineering field.

Language Resources Project Manager - Junior

Under the supervision of the Language Resources Manager, the Language Resources Project Manager ? Junior will be in charge of the identification of Language Resources (LRs), the negotiation of rights in relation with their distribution, as well as the data preparation, documentation and curation.

The position includes, but is not limited to, the responsibility of the following tasks:
?    Identification of LRs and Cataloguing
?    Negotiation of distribution rights, including interaction with LR providers, drafting of distribution agreements, definition of prices of language resources to be integrated in the ELRA catalogue or for research projects
?    LR Packaging within production projects
?    Data preparation, documentation and curation

Profile:
?    Master Degree or Equivalent in computational linguistics or similar fields
?    Experience in managing NLP tools
?    Good knowledge of script programming (Perl, Python or other languages)
?    Good knowledge of Linux
?    Dynamic and communicative, flexible to combine and work on different tasks
?    Ability to work independently and as part of a team
?    Proficiency in English, with strong writing and documentation skills. Communication skills required in a French-speaking working environment
?    Citizenship of (or residency papers) a European Union country

The position is to be filled as soon as possible and it is based in Paris.

Salary: between 25K? and 30K?

Applicants should email a cover letter addressing the points listed above together with a curriculum vitae to: job@elda.org

ELDA is a human-sized company (15 people) acting as the distribution agency of the European Language Resources Association (ELRA). ELRA was established in February 1995, with the support of the European Commission, to promote the development and exploitation of Language Resources (LRs). Language Resources include all data necessary for language engineering, such as monolingual and multilingual lexica, text corpora, speech databases and terminology. The role of this non-profit membership Association is to promote the production of LRs, to collect and to validate them and, foremost, make them available to users. The association also gathers information on market needs and trends.

More opportunities at ELDA: http://www.elra.info/en/opportunities/

For further information about ELDA/ELRA, visit: http://www.elda.org

Back

Top

Job offer: Automatic assessment of the fluency of young readers (H/F)

We are offering a two-years Postdoc position at GIPSA-Lab, Grenoble, funded by the e-FRAN Fluence project, aiming at providing computer-assisted reading in the classroom. We will monitor the reading performance of 750 3th grade readers and 400 high school pupils with reading difficulties.
Postdoc Topic: Automatic text-to-speech alignment for assessing reading fluency
Expected qualifications: Applicants for the PostDoc position (3 years) must have a PhD in speech recognition with a strong background on Machine Learning and speech processing. Knowledge of computer-assisted language learning (CALL) applications and processing of children voices is a plus.
Applications: The candidates should apply though the CNRS portal: https://emploi.cnrs.fr/Offres/CDD/UMR5216-ALLBEL-004/Default.aspx?lang=EN. Feel free to contact : gerard.bailly@gipsa-lab.fr for additional information . Only shortlisted candidates will be contacted.
Expected starting date: The position is open and is to be filled ASAP.
Duration: 24 months
Gross salary: 2500?/month

Back

Top

Le GIPSA cherche un(e) jeune docteur(e), spécialiste de reco automatique pour le projet e-FRAN Fluence pour l'évaluation de la fluence de jeunes lecteurs.
  Merci de diffuser cette offre de PostDoc de 12 mois, renouvelable une fois (https://emploi.cnrs.fr/Offres/CDD/UMR5216-ALLBEL-004/Default.aspx) aux post-docs de vos labos potentiellement concerné(e)s/intéressé(s)s

Back

Top

La section recherche de Qwant propose un stage et un poste permanent en Traitement Automatique des Langues dans le cadre applicatif de la Recherche d'Information, lié à des problématiques d'analyse de sentiments/d'opinion, traduction automatique, l'extraction d'information sémantique, agents conversationnels, etc. Ces postes sont liés à nos projets de recherche avec l'INRIA & européens H2020 dans lesquels ces problématiques seront explorées.

Les postes à pourvoir seront situés à Paris, dans nos locaux près de la Porte Dauphine au 7 rue Spontini. L'équipe TAL est au cœur d'un écosystème dynamique de recherche applicatif composé, entre autres, des équipes de Traitement de l'Image, Apprentissage Automatique pour la Recherche d'Information, Protection de la vie Privée, et Cartographie. Les équipements comprennent notamment un cluster de calcul GPU dernier cri.

Pour toute information complémentaire, vous pouvez me contacter directement à cette adresse : c.servan_CHEZ_qwant.com.

Back

Top

Subject: PhD Position to work with laryngeal high-speed videos of pathological speakers at the MUV, Vienna, Austria.

Job description:

The Medical University of Vienna (MUV), Austria, seeks to fill a position of a PhD-student within the project ?Objective differentiation of dysphonic voice quality types?. The candidate must hold a master?s degree, preferably in (one of) the fields of sound engineering, acoustical engineering, audio signal processing, or similar. The work will be conducted at the Division of Phoniatrics-Logopedics within the Department of Otorhinolaryngology of the MUV.

The workgroup hosting the project is interested in the assessment of voice parameters relevant to the medical diagnosis and clinical care of voice disorders. A focus is given to functional assessment of voice, especially to the objective description of voice quality. The levels of description include kinematics of voice production, voice acoustics, and auditory perception of voice. Clinical studies are conducted with a laryngeal high-speed camera that records vocal fold vibration at 4000 frames per second. Microphone signals of the voice are recorded in parallel. Vibratory patterns of the vocal folds are analysed visually and computationally via modelling. Trajectories of vocal fold edges, spatial arrangements thereof, and glottal area waveforms are analysed. Regarding acoustics, analysis of audio recordings involves the implementation, testing, and training of specialized synthesizers for pathological voices. On the level of auditory perception, listening experiments are conducted, especially experiments involving discrimination tasks.

Mandatory skills of the candidate are MATLAB programming, speech signal processing, psychoacoustics, good knowledge of English, good communication skills, and excellent analytical thinking. Optional skills of the candidate are knowledge of German, experience in a health care profession, image and video processing, Python, PureData, object-oriented programming, software engineering, version control (Subversion, Git, or similar), SQL, and XML.

The project duration is 4-5 years. The Austrian Science Fund (FWF) budgets for doctoral candidates a gross salary of 2.112,40 Euro per month. Application documents can be submitted to philipp.aichinger@meduniwien.ac.at by October 31^st, 2018. Interviews are planned for November 2018.The project is planned to start in December 2018.

Information regarding the beautiful city of Vienna can be found at https://www.meduniwien.ac.at/web/en/international-affairs/living-in-vienna/.

Back

Top

The European Language resources Distribution Agency (ELDA), a company specialized in Human Language Technologies within an international context is currently seeking to fill an immediate vacancy for a Research and Development Engineer.

Under the supervision of the CEO, the responsibilities of the R&D Engineer include designing, developing, documenting, deploying and maintaining tools, software components or applications for Language Resource production and management.
He/she will be in charge of managing the current Language Resources production workflows and co-ordinating ELDA?s participation in R&D projects while being also hands-on whenever required by the language resource production and management team. He/she will liaise with external partners at all phases of the projects (submission to calls for proposals, building and management of project teams) within the framework of international, publicly- or privately-funded research and development projects.

This yields excellent opportunities for creative and motivated candidates wishing to participate actively to the Language Engineering field.

Profile:

•     PhD in Computer Science, Natural Language Processing, or equivalent
•     Experience in Natural Language Processing (speech processing, data mining, machine translation, etc.)
•     Proficiency in classic shell scripting in a Linux environment (POSIX tools, Bash, awk)
•     Good level in Python
•     Knowledge of a distributed version control system (Git, Mercurial)
•     Knowledge of SQL and of RDBMS (PostgreSQL preferred)
•     Knowledge of XML and of standard APIs (DOM, SAX)
•     Familiarity with open source and free software
•     Knowledge of a statically typed functional programming language (OCaml preferred) is a plus
•     Good level in French and English, with strong writing and documentation skills in both languages
•     Dynamic and communicative, flexible to work on different tasks in parallel
•     Ability to work independently and as part of a multidisciplinary team
•     Citizenship (or residency papers) of a European Union country

Permanent position. Applications will be considered until the position is filled.

Salary is commensurate with qualifications and experience.
Benefits: complementary medical insurance; meal vouchers.

Applicants should email a cover letter addressing the points listed above together with a curriculum vitae to:

ELDA
9, rue des Cordelières
75013 Paris
FRANCE
Fax : 01 43 13 33 30
Mail job@elda.org

ELDA is acting as the distribution agency of the European Language Resources Association (ELRA). ELRA was established in February 1995, with the support of the European Commission, to promote the development and exploitation of Language Resources (LRs). Language Resources include all data necessary for language engineering, such as monolingual and multilingual lexica, text corpora, speech databases and terminology. The role of this non-profit membership Association is to promote the production of LRs, to collect and to validate them and, foremost, make them available to users. The association also gathers information on market needs and trends.

For further information about ELDA and ELRA, visit:
http://www.elra.info

Back

Top

Master R2 internship in Natural Language Processing: Introduction of semantic

information in a speech recognition system

Supervisors: Irina Illina, MdC, Dominique Fohr, CR CNRS

Team: Multispeech, LORIA-INRIA

Contact: illina@loria.fr, dominique.fohr@loria.fr

Duration: 5-6 months

Deadline to apply : January 20th, 2019

Required skills: background in statistics, natural language processing and computer program skills (Perl, Python). Candidates should email a detailed CV with diploma

Motivations and context

Semantic and thematic spaces are vector spaces used for the representation of words, sentences or textual documents. The corresponding models and methods have a long history in the field of computational linguistics and natural language processing. Almost all models rely on the hypothesis of statistical semantics which states that: statistical schemes of appearance of words (context of a word) can be used to describe the underlying semantics. The most used method to learn these representations is to predict a word using the context in which this word appears [Mikolov et al., 2013b, Pennington et al., 2014], and this can be realized with neural networks. These representations have proved their effectiveness for a range of natural language processing tasks [Baroni et al., 2014]. In particular, Mikolov?s Skip-gram and CBOW models et al. [Mikolov et al., 2013b, Mikolov et al., 2013a] have become very popular because of their ability to process large amounts of unstructured text data with reduced computing costs. The efficiency and the semantic properties of these representations motivate us to explore these semantic representations for our speech recognition system.

Robust automatic speech recognition (ASR) is always a very ambitious goal. Despite constant efforts and some dramatic advances, the ability of a machine to recognize the speech is still far from equaling that of the human being. Current ASR systems see their performance significantly decrease when the conditions under which they were trained and those in which which they are used differ. The causes of variability may be related to the acoustic environment, sound capture equipment, microphone change, etc.

Objectives

The speech recognition (ASR) stage will be supplemented by a semantic analysis to detect the words of the processed sentence that could have been misrecognized and to find words having similar pronunciation and matching better the context. For example, the sentence « Silvio Berlusconi, prince de  Milan » can be recognized by the speech recognition system as : « Silvio Berlusconi, prince de mille ans ». Good semantic context representation of the sentence could help to find and correct this error.

The Master internship will be devoted to the innovative study of the taking into account of semantics through predictive representations that capture the semantic features of words and their context. Research will be conducted on the combination of semantic information with information from denoising to improve speech recognition. As deep neural networks (DNNs) can model complex functions and get outstanding performance, they will be used in all our modeling.

References

[Deng, 2014] Deng, L. Deep learning: Methods and applications. Foundations and Trends in Signal Processing, 7(3-4), 197?387, 2014.

[Goodfellow et al., 2016] Goodfellow, I., Bengio, Y., & Courville, A. Deep Learning. MIT Press. http://www.deeplearningbook.org, 2016.

[Mikolov et al., 2013a] Mikolov, T. Chen, K., Corrado, G., and Dean, J. Efficient estimation of word representations in vector space, CoRR, vol. abs/1301.3781, 2013.

[Mikolov et al., 2013b] Mikolov, T., Sutskever, I., Chen, T. Corrado, G.S.,and Dean, J. Distributed representations of words and phrases and their compositionality, in Advances in Neural Information Processing Systems, 2013, pp. 3111?3119.

[Pennington et al., 2014] Pennington, J., Socher, R., and Manning, C. (2014). Glove: Global vectors for word representation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532-1543.

[Povey et al, 2011] Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., Hannemann, M., Motl?cek, P., Qian, Y., Schwarz, Y., Silovsky, J., Stemmer, G., Vesely, K. The Kaldi Speech Recognition Toolkit, Proc. ASRU, 2011.

[Sheikh, 2016] Sheikh, I. Exploitation du contexte sémantique pour améliorer la reconnaissance des noms propres dans les documents audio diachroniques?, These de doctorat en Informatique, Université de Lorraine, 2016.

[Sheikh et al., 2016] Sheikh, I. Illina, I. Fohr, D. Linares, G. Learning word importance with the neural bag-of-words model, in Proc. ACL Representation Learning for NLP (Repl4NLP) Workshop, Aug 2016.

Back

Top

L'équipe SAMoVA de l'IRIT (Institut de Recherche en Informatique de Toulouse) recrute un chercheur ou une chercheuse en post-doctorat pour le projet collaboratif LinTo (PIA - Programme d?Investissements d'Avenir), projet d?assistant conversationnel destiné à opérer en contexte professionnel pour proposer des services en lien avec le déroulement de réunions.  

Back

Top

https://vacancies.essex.ac.uk/tlive_webrecruitment/wrd/run/ETREC107GF.open?VACANCY_ID=271640IULU&WVID=9918109NEm&LANG=USA

Back

Top

L'équipe Multispeech recrute un post-doctorant sur l'articulation de l'allemand par des français

Etude articulatoire des sons de l?allemand prononcés par des français

Contexte

L?équipe MULTISPEECH, de Inria Nancy-Grand Est, étudie différents aspects de la modélisation de la parole, comme la production, la reconnaissance et la synthèse de la parole. Dans le cadre du projet collaboratif e-FRAN METAL qui porte sur l?utilisation du numérique dans l?éducation, des techniques de la parole sont adaptées, enrichies, et mises en ?uvre pour aider à l?apprentissage d?une langue étrangère. L?objectif consiste alors à détecter les défauts de prononciation des apprenants (prononciation des sons et intonation), et à proposer des diagnostics pour aider l?apprenant à améliorer sa prononciation.

Missions

Dans ce cadre, une étude articulatoire portera sur la prononciation des sons de l?allemand par des locuteurs français. Plus précisément, une partie de l?étude concernera les consonnes fricatives qui ne sont pas présentes dans le système français, notamment les fricatives palatale (le [ç] de « ich ») et vélaire (le [x] de « Buch »). Afin d?analyser les interférences entre la langue première et la langue seconde sur le plan articulatoire,  la prononciation des consonnes proches de celles étudiées, que ce soit dans le système natif des apprenants (le français) ou le système non natif (l?allemand) fera également partie de l?étude.

Le travail commencera par une collecte de données articulatoires. Pour ce faire, le chercheur utilisera un articulographe qui fait partie d?une plateforme d?acquisition multimodale disponible au laboratoire d?accueil. L?analyse des sorties de l?articulographe se fera grâce au logiciel VisArtico qui a été développé au sein de l?équipe. Les données acoustiques, synchronisées avec les données articulatoires, seront recueillies et analysées. Enfin, une étude perceptive restreinte sera menée afin mettre en correspondance articulation et perception.

L?étude doit permettre une meilleure compréhension des difficultés articulatoires rencontrées par les français parlant allemand en fonction des interférences entre les deux langues. Elle doit également permettre de présenter des stratégies pour prendre en compte ces difficultés afin d?améliorer  la qualité des retours faits aux apprenants dans le domaine de l?apprentissage de l?oral d?une langue étrangère. Les résultats seront appliqués directement dans le projet e-FRAN METAL.

Prérequis : le candidat devra avoir soutenu sa thèse entre 2015 et 2018.

Durée du poste à pourvoir : 18 mois,

Début : dès que possible et au plus tard fin mars 2019 

Lieu : INRIA Grand-Est, Nancy, France.

Encadrants : Anne Bonneau (anne.bonneau@loria.fr) et Slim Ouni (slim.ouni@loria.fr)

Profil et compétences recherchées :

-        Doctorat portant sur la phonétique ou le traitement de la parole (thèse soutenue thèse entre 2015 et 2018)

-        Bonnes connaissances en phonétique

-        Des connaissances de base en programmation (pouvoir écrire des scripts simples)

-        des bases en langues française et allemande.

Références bibliographiques.

D Jouvet, A. Bonneau, J. Trouvain, F. Zimmerer, Y. Laprie, B. Moebius. 'Analysis of phone confusion matrices in a manually annotated French-German learner corpus?. Workshop on Speech and Language Technology in Education, Sep 2015, Leipzig, Germany. Proceedings SLaTE 2015, Workshop on Speech and Language Technology in Education. <hal-01184186>

S. Ouni, L. Mangeonjean, I. Steiner. VisArtico: a visualization tool for articulatory data. 13th Annual Conference of the International Speech Communication Association ? InterSpeech 2012, Sep 2012, Portland, OR, United States. 2012.

Wieling, M., Veenstra, P., Adank, P., and Tiede, M.  (2015), 'Comparing L1 and L2 speakers using articulography' Proceedings of the International Congress of Phonetic Sciences (ICPhS).Glasgow.  

Back

Top

Étude et développement d’une solution de synthèse vocale pour l’imitation d’un locuteur
 
Stage de fin d’études d’Ingénieur ou de Master 2 – Année académique 2018-2019
 
 
Mots clés : Synthèse vocale (Text-to-speech – TTS), Adaptation de locuteur, Deep Neural Network (DNN), Machine Learning
 
Contexte
 
L’Institut national de l’audiovisuel (Ina) est un établissement public à caractère industriel et commercial (EPIC) dont la mission principale consiste à archiver et valoriser la mémoire audiovisuelle française (radio, télévision et web média). À ce jour, plus de 17 millions d’heures de documents télé et radio ont été conservées.
 
Ce stage s’inscrit dans le cadre du projet Saphir de restauration d’anciens disques gravés. Un certain nombre de ces disques sont fracturés et certaines portions des disques sont manquantes. La finalité du projet global consiste à utiliser des technologies de synthèse vocale pour combler les parties du signal pour lesquelles le support est manquant, ou trop endommagé pour pouvoir être décodé.
 
Objectifs du stage
 
Le but du projet est de proposer et d’implémenter une solution de synthèse paramétrique par réseaux de neurones profonds (DNN) pour la création d’un modèle de voix universel en français à partir d’un large corpus et pour l’adaptation de ce modèle vers une locuteur particulier à partir d’un corpus de taille réduite (entre 1 et 5 minutes de parole). Il sera aussi nécessaire d’évaluer l’influence de la taille du corpus utilisé pour l’adaptation sur la qualité de la synthèse résultante. 
 
Le stage sera organisé en plusieurs étapes :
 
• Réaliser un état de l’art de la synthèse par modèles statistiques (HMM, DNN) • Réaliser un état de l’art de l’adaptation des systèmes de synthèse à un locuteur particulier • Faire l’inventaire des corpus (parole + transcription) de l’Ina (ou extérieurs en accès libre) qui pourraient servir à l’entrainement des modèles de synthèses • Entrainer un modèle universel (Universal background model – UBM) à partir d’un large corpus (2h - 50h) de plusieurs locuteurs • Adapter le modèle vers un locuteur cible à partir d’un corpus de taille réduite (1min – 5min) • Évaluer l’influence de la taille du corpus d’adaptation pour la qualité de la synthèse résultante
 
Le langage de programmation utilisé dans le cadre de ce stage sera Python. Le stagiaire aura accès aux ressources de calcul de l’Ina (serveurs et clusters), ainsi que d’un desktop performant avec 2 GPU de génération récente.
 
Possibilité de poursuivre en thèse CIFRE selon les résultats du stage et les offres disponibles.
 
Valorisation du stage
 
Différentes stratégies de valorisation des travaux du stagiaire seront envisagées, en fonction du degré de maturité des travaux réalisés : • Diffusion des outils d’analyse réalisés sous licence open-source via le dépôt GitHub de l’Ina : https://github.com/ina-foss • Rédaction de publications scientifiques.
 
Conditions du stage
 
Le stage se déroulera sur une période de 4 à 6 mois, au sein du service de la Recherche de l’Ina. Il aura lieu sur le site Bry2, situé au 18 Avenue des frères Lumière, 94366 Bry-sur-Marne. Le stagiaire sera encadré par Marc Evrard (mevrard@ina.fr). Gratification : environs 550 Euros par mois.
 
Profil recherché
 
Bac +5 dans le domaine de l’informatique et de l'IA. Compétence en langage Python et expérience dans l’utilisation de bibliothèques de machine learning et big data. Capacité à réaliser une étude bibliographique à partir d’articles scientifiques rédigés en anglais.
 
Bibliographie
 
Chenot, J.-H., Laborelli, L., Noiré, J.-E. (2018). Saphir: Optical Playback of Damaged and Delaminated Analogue Audio Disc Records,  ACM Journal on Computing and Cultural Heritage (JOCCH) vol.11, no. 3, August 2018. <https://hal.archives-ouvertes.fr/hal-01885324>.
 
Ze, H., Senior, A., & Schuster, M. (2013). Statistical parametric speech synthesis using deep neural networks. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 7962–7966). Vancouver, BC, Canada: IEEE. https://doi.org/10.1109/ICASSP.2013.6639215
 
Wang, Y., Skerry-Ryan, R. J., Stanton, D., Wu, Y., Weiss, R. J., Jaitly, N., Saurous, R. A. (2017). Tacotron: Towards End-to-End Speech Synthesis. In Interspeech.
 
Wu, Z., Swietojanski, P., Veaux, C., Renals, S., & King, S. (2015). A Study of Speaker Adaptation for DNN-Based Speech Synthesis, 5. In Interspeech.

Back

Top

Postdoctoral researcher and doctoral student positions in ubiquitous computing and
computer vision at Idiap, Switzerland


The Social Computing Group at Idiap is seeking creative and motivated young researchers
(postdocs and doctoral students) for two topics:

Topic 1: This is an opening for ubiquitous computing researchers (postdoc or doctoral
student) to work on machine learning applied to smartphone sensor data for large-scale
behavioral analysis in daily life, with experience in machine learning for activity
recognition, deep learning, and mobile data science, in the context of a large European
project.

Topic 2: This is an opening for a computer vision or multimodal interaction postdoc
researcher to work on social interaction analysis, with experience in deep learning
applied to face, body, and gesture analysis, in the context of an innovation project with
an industrial partner.

The positions offer the opportunity to do exciting work on human-centered analysis of
everyday life behavior. The researchers will collaborate with Prof. Daniel Gatica-Perez
and his research group. The candidates will have degrees in computer science or
engineering, with experience in machine learning, data science, and ubicomp (for topic
1); and computer vision and machine learning for multimodal interaction (topic 2).
Postdoctoral researchers are expected to have a strong publication record. Candidates to
the PhD student position will become doctoral students at EPFL contingent upon acceptance
by the EPFL Doctoral School. The applicants will have strong programming skills (Python,
C/C++ in the Linux environment.) Experience with deep learning (TensorFlow, pyTorch) is a
definite asset.

Salaries are competitive and starting date is immediate. Interviews will start upon
reception of applications until the positions are filled.

Interested candidates are invited to submit a cover letter, a detailed CV, and the names
of three references through the Idiap online recruitment system:
http://www.idiap.ch/en/allnews/postdoctoral-researcher-doctoral-student-positions-in-ubiquitous-computing-and-computer-vision. Interested candidates can also contact Prof. Daniel Gatica-Perez
(gatica@idiap.ch).

About Idiap

Idiap is an independent, not-for-profit, research institute funded by the Swiss Federal
Government and the State of Valais. The institute is located in Martigny in Valais, a
scenic region in the south of Switzerland, surrounded by the highest mountains of Europe,
which offers exceptional quality of life, exciting recreational activities as well as
varied cultural activities, and within close proximity to Lausanne and Geneva.

Idiap is an equal opportunity employer, and offers competitive salaries and conditions in
a young, dynamic, and multicultural environment. English is the working language.

For frequently asked questions (FAQs) about working and living in Switzerland, please go
to http://www.idiap.ch/en/faq

Back

Top

Aalto University Aalto University is a community of bold thinkers where science and art meet technology and business. We are committed to identifying and solving grand societal challenges and building an innovative future. Aalto University has been ranked the 9th best young university in the world (Top 50 under 50, QS 2018) and one of the world’s top technology challenger universities (THE 2017), thinking outside the box on research collaboration, funding and innovation. Aalto has six schools with nearly 11 000 students and 4000 employees of which close to 400 are professors. Our campuses are located in the Capital Area of Finland. With 37% of our academic faculty coming from outside Finland, we are a highly international community with strong academic standing.
 
At Aalto, high-quality research, art, education and entrepreneurship are promoted hand in hand. Disciplinary excellence is combined with multidisciplinary activities, engaging both students and the local innovation ecosystem. Our main campus is quickly transforming into an open collaboration hub that encourages encounters between students, researchers, industry, startups and other partners. Aalto University was founded in 2010 as three leading Finnish universities, Helsinki University of Technology, the Helsinki School of Economics and the University of Art and Design Helsinki, were merged to strengthen Finland’s innovative capability.
 Aalto University School of Electrical Engineering invites applications for an 

 ASSOCIATE OR ASSISTANT PROFESSOR IN SPEECH AND LANGUAGE TECHNOLOGY
 
We are looking for an associate or assistant professor to establish and lead a group of researchers and students within the speech and language technology research area at Aalto University. In this position, you will have a chance to make an impact in academic research and teaching as well as in society for example by industrial research collaboration. At Aalto University, you will have excellent research facilities and opportunities for interdisciplinary research with top-level researchers in machine learning, signal processing, acoustics, neuroscience and human-computer interfaces. 
 YOUR ROLE AND GOALS Your tasks and responsibilities include conducting outstanding research and teaching as well as preparing research projects with funding from international and national sources. You supervise and recruit postdocs and PhD students and participate in teaching within the Computer, Communication and Information Science Master's Programme which is one of the most competitive at Aalto University. 
 SCIENTIFIC ENVIRONMENT The professorship is situated at the Department of Signal Processing and Acoustics (in School of Electrical Engineering) where currently two out of ten tenured/tenure track professors work in the area of speech and language technology. Academy Professor Paavo Alku leads a group in speech communication technology and text-to-speech synthesis. The group is known particularly for its voice source research and its technical and interdisciplinary applications. Professor Mikko Kurimo leads a group in speech recognition and language modeling. His group is best known for developing successful language-independent models of morphologically rich languages and winning the MGB 2017 speech recognition challenge. These groups are also very well connected to the recently founded Finnish Centre of Artificial Intelligence (FCAI), which is a large collaboration effort for professors in machine learning and speech and language technology in both Aalto University and University of Helsinki. 
 YOUR EXPERIENCE AND AMBITIONS We expect a strong track record of publications and achievements in speech and language technology, excellent teaching skills to help students to learn difficult topics, and motivation and competence to start and lead new and highly ambitious research projects aiming at significant scientific results and impacts. The professorship is open for qualified applicants from all areas of speech and language technology but we prioritize such fields, which enable research collaboration with the Department’s current groups in speech and language technology.
All applicants must have a doctorate in speech and language technology (or in a related area of engineering) and fluent command in English. 
 
If you wish to hear more about the position, you can contact Academy Professor Paavo Alku or Professor Mikko Kurimo (firstname.lastname@aalto.fi). In recruitment process related questions, please contact HR Coordinator Saara Haggrén (firstname.lastname@aalto.fi). 
 READY TO APPLY? If you want to join our community, please submit your application through our eRecruitment system no later than 31 March 2019. 
 To apply, please share the following application materials with us:
 1. Cover letter 2. Curriculum vitae (with contact information and ResearcherID number) 3. List of publications in which the 7 most significant publications are highlighted 4. A research statement describing past research and plans for future research  5. A teaching portfolio describing teaching experience and plans for teaching 6. Contact information of possible references or at most 2 reference statements
 
All application materials should be submitted in English, in pdf format. The applications for the tenure track positions are to be addressed to the President of Aalto University. 
 
From amongst the applicants in the first phase, Aalto University will select those who will be asked to visit Aalto University in May/June 2019. 
 
Short-listed candidates’ applications will be submitted for review by external experts (the second phase of the application process). 
 
General instructions for applicants including evaluation criteria, language requirements and guidelines for compiling teaching portfolio and CV are given at https://www.aalto.fi/tenure-track.  
 
Aalto University reserves the right for justified reasons to leave the position open, to extend the application period and to consider candidates who have not submitted applications during the application period.
 
 
 
As a living and working environment, Finland consistently ranks high in quality-of-life. For more information about living in Finland: https://www.aalto.fi/services/about-finland.

Back

Top

 Mines Nancy et le LORIA recrutent un MCF section 27 avec un profil IA / Deep Learning
théorique ou appliqué (parole, texte, image, etc):
http://www.loria.fr/wp-content/uploads/2018/12/Fiche-de-poste-MCF-27-0416-Mines-LORIA.pdf

Back

Top

The Department of Computer Science at the University of Colorado Boulder anticipates hiring a full time postdoctoral fellow starting in Summer/Fall 2019 for one year and renewable for a second year. This position will work with Dr. Sidney D?Mello https://www.colorado.edu/ics/sidney-dmello and will play a collaborative and co-leadership role in a vibrant research team encompassing researchers in Computer Science, Cognitive Science, Psychology/Neuroscience, and Education.

Who we are:

=========

The mission of the Institute of Cognitive Science (ICS) at CU-Boulder is to understand and enhance human cognition, learning, and development through the creation of interdisciplinary partnerships.  ICS fosters rich scientific interchange across researchers from a broad range of disciplines including Artificial Intelligence, Linguistics, Psychology, Neuroscience, Computer Science, Philosophy, and Education.

What your key responsibilities will be:

============================

Develop computational modeling and machine learning techniques to model behavioral and mental states (e.g., affect, attention, workload) from multimodal data (e.g., video, audio, physiology, eye gaze) across a range of interaction contexts (e.g., learning from educational games, collaborative problem solving, everyday activities in the wild).

This position offers a unique postdoctoral training experience and unsurpassed publishing opportunities within multi-department and multi-institution grant-funded projects. The candidate will be encouraged to develop advanced technical skills, strengthen their research portfolios via peer-reviewed publications, gain interdisciplinary experience by working with a diverse team, develop leadership skills by mentoring students, and gain expertise in co-authoring grant proposals.

Requirements:

===========

- Ph.D. in Computer Science/Machine Learning or a related field (at the time of hire)

- Research experience in computational modeling and advanced machine learning (e.g., graphical models, deep recurrent neural networks).

- Strong writing skills and ability to conduct independent research

as evidenced by first author publications.

Desired:

- Research experience in one or more of the following areas (computer vision, computational psychophysiology, natural language processing, speech processing).

- Background in interdisciplinary research.

- Experience mentoring graduate and undergraduate students.

Job details:

========

- 1-2 year position. Initial contract is for one year (providing renewal after 6-month probationary period). Second year contract is based on performance and extension to a third year is possible.

- Start date is negotiable, but anticipated for Summer/Fall 2019.

- Competitive salary commensurate with experience and full benefits.

Application:

=========

To apply, please submit the following materials through CU Boulder Jobs:

- Resume/CV

- Cover Letter

- PDF Sample of Work: Two representative publications.

During the application process you will need to enter contact information for three references and we will request letters of recommendation and additional materials, if needed, as the search progresses.

Review of applications will begin immediately and will continue until the position is filled.

===================================

Be Accomplished. Be Resourceful. Be Boulder.

https://www.colorado.edu/ics/

Back

Top

Postdoc in Speech Production (M/F)

Reference : UMR7018-CECFOU-004
Workplace : PARIS 05
Date of publication : Monday, January 07, 2019
Type of Contract : FTC Scientist
Section CN : Sciences du langage
Contract Period : 12 months
Expected date of employment : February/March 2019
Proportion of work : Full time
Remuneration : between 2600 et 3600? (brut) per month according to experience
Desired level of education : PhD
Experience required : Indifferent

Missions

The post-doctoral fellow will conduct experiments on adaptation to speech disturbances and production conditions, aimed at testing the integrity/flexibility of speech units and their variability according to their structural/motor complexity or frequency. Speech will be compared with other non-verbal movements and data from healthy speakers will be compared with data from speakers with different motor speech disorders.

Activities

As part of the MoSpeeDi project, the Laboratoire de Phonétique et Phonologie (LPP - CNRS/Sorbonne Nouvelle) in Paris is offering a full-time postdoctoral position for 12 months (with a possible extension of a few months).
The post-doctoral fellow will be in charge of designing, carrying out and processing experiments in collaboration with the other members of the project.
The overall objective of the project is to better understand the processes and representations at play during speech production, focusing on the final stages of the process where the encoded linguistic message is transformed into articulated speech. At the interface between linguistic and motor processes, these steps are also associated with various Motor Speech Disorders (MSD, dysarthria and speech apraxia). Articulation and acoustic data will be collected and analyzed experimentally for healthy and MSD speakers to (a) better understand the phonetic and motor speech planning and programming stages, (b) identify markers of these processes, and (c) better isolate and categorize speech disorders in MSDs.

Skills

Required skills

- PhD in phonetics or on a subject related to speech production or speech motor control.
- Good knowledge of speech production and motor control models, particularly on adaptation phenomena and/or speech motor disorders.
- Experience in signal processing (acoustic and/or articulatory)
- Programming skills (e. g. with Praat, Matlab, Python or R)
- Strong statistical analysis skills and good writing skills
- Basic knowledge of French and excellent proficiency in English

Work Context

The candidate will work closely with Cécile Fougeron and Leonardo Lancia.
The Phonetics and Phonology Laboratory (CNRS/Université Sorbonne-Nouvelle), located in the 5th arrondissement in Paris, is a research, research training and teaching unit in experimental phonetics and phonology.

Application process and contact information

Applications can be submitted online at the following address: http://bit.ly/2C7LXSh. For more information, the candidates can contact Cécile Fougeron (cecile.fougeron@sorbonne-nouvelle.fr) and Leonardo Lancia (leonardo.lancia@sorbonne.nouvelle.fr).

Back

Top

IDIAP has openings for at least one post-doc in the general area of speech and
natural language processing.  The work will involve investigating how to interface
current end-to-end speech recognition technology with its counterparts in natural
language processing; it would suit someone from either discipline.  The posts are
research oriented, but funded by industrial collaborations.
More information along with application instructions are at the URL:
 http://www.idiap.ch/education-and-jobs/job-10251

Idiap is located in Martigny in French speaking Switzerland, although the lab hosts many
nationalities, and functions in English.  All positions offer quite generous salaries.

Several similar positions at PhD, post-doc and senior level are available at the
institute in general.
 http://www.idiap.ch/en/join-us/job-opportunities

Back

Top

Post-doctoral research position:

Extremely-low-resource radio browsing for humanitarian monitoring

Stellenbosch University, South Africa

 A post-doctoral research position focussing on the automatic identification of spoken keywords in multilingual environments with extremely few or even no resources using state-of-the-art architectures is available in the Digital Signal Processing Group of the Department of Electrical and Electronic Engineering at the University of Stellenbosch. This is part of an ongoing project to develop wordspotters that can be used to monitor community radio broadcasts in rural African regions as a source of early warning information during natural disasters, disease outbreaks, or other crises.  This phase of the project will consider several languages spoken in Mali, at least some of which are severely under-resourced and have not been the subject of speech technology research before. Specific project objectives include the development of a research system, the development of deployable system, the development of new methods and techniques and the production of associated publishable outputs. The position is part of a collaborative project with the United Nations Global Pulse. References to papers already produced as part of the project are listed below, and some general further information is available at http://pulselabkampala.ug/.

Applicants must hold a PhD (obtained within the last 5 years) in the field of Electronic/Electrical Engineering, Information Engineering, or Computer Science, or other relevant disciplines. Suitable candidates must have practical experience with automatic speech recognition systems in general and deep neural net architectures in particular, and should have an excellent background in statistical modelling and machine learning. The candidate must also have good programming skills and be able to use high level programming languages for developing prototype systems. Finally, candidates must have excellent English writing skills and have an explicit interest in scientific research and publication.

The position will be available for one year, with a possible extension to a second year, depending on progress and available funds.  

Applications should include a covering letter, curriculum vitae, list of publications, research projects, conference participation and details of three contactable referees and should be sent as soon as possible to: Prof Thomas Niesler, Department of Electrical and Electronic Engineering, University of Stellenbosch, Private Bag X1, Matieland 7602. Applications can also be sent by email to: trn@sun.ac.za. The successful applicant will be subject to University policies and procedures.

Interested applicants are welcome to contact me at the above e-mail address for further information regarding the project.

References:

1. Menon, R; Biswas, A; Saeb, A; Quinn, J; Niesler, T.R. Automatic Speech Recognition for Humanitarian Applications in Somali. Proceedings of SLTU, Gurugram, India, August 2018.

2. Menon, R; Kamper, H; Yilmaz, E; Quinn, J; Niesler, T.R. ASR-free CNN-DTW keyword spotting using multilingual bottleneck features for almost zero-resource languages.. Proceedings of SLTU, Gurugram, India, August 2018.

3. Menon, R; Kamper, H; Quinn, J; Niesler, T.R. Fast ASR-free and almost zero-resource keyword spotting using DTW and CNNs for humanitarian monitoring. Proceedings of Interspeech, Hyderabad, India, September 2018.

4. Saeb, A; Menon, R; Cameron, H; Kibira, W; Quinn, J; Niesler, T.R. Very low resource radio browsing for agile developmental and humanitarian monitoring. Proceedings of Interspeech, Stockholm, Sweden, August 2017.

5. Menon, R; Saeb, A; Cameron, H; Kibira, W; Quinn, J; Niesler, T.R. Radio-browsing for Developmental Monitoring in Uganda. Proceedings of the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, USA, March 2017.

Back

Top

Naples (Italian: Napoli) is the third largest city in Italy, and is the capital of the Campania region. World-known for its rich history, art, culture, architecture, music, and gastronomy, Naples is a lively, exciting and bustling city situated on the southwest coast in a gorgeous gulf, and is surrounded by attractive tourist and archaeological sites such as Capri, Ischia, Amalfi Coast, Pompei, Ercolano, Mount Vesuvius. See https://www.visitnaples.eu/en for further information.

Back

Top

Sujet de stage M2 : sleepiness detection and characterization in voice recordings.

Advisors: - Jean-Luc Rouas – CR CNRS, LaBRI : rouas@labri.fr

- Pr. Pierre Philippe – PU-PH, SANPSY : pr.philip@free.fr

Subject: Detection sleepiness is useful for many reasons : for instance, it can help prevent road traffic accidents, it can be useful to monitor workers in critical environments (air traffic control, nuclear plants, etc.). While these applications are very important, it can also be used in a clinical way in the follow-up of sleep deprived patients. The Obstrusive Sleep Apnea is nowaday recognised as a major public health problem resulting in many consequences : road traffic accidents, increase in heart failure rates, behavioural and cognitive troubles, … In order to deal with these problems, we devised an experiment with the SANPSY research unit (Sommeil - Addiction- Neuropsychiatrie) Université Bordeaux Ségalen CNRS USR 3413) in order to assess if we can evaluate the sleepiness level of a patient using only a simple speech recording. Previous research has shown that this task is possible, however most studies on sleepiness detection from speech rely on corpora with self reported labels according to the KSS scale [1]. For instance, the Interspeech 2011 speaker state challenge [2] uses data from 99 speakers and contains mixed data from different tasks (isolated vowels, read speech, command request, spontaneous speech) in German. The annotations are self-reported using the KSS scale and are divided in two classes : sleepy (S) and not sleepy (NS). The best system [3] in the challenge competition won with a reported accuracy slightly above the baseline, around 72 % of correctly identified samples. Other efforts on sleepiness detection from speech often use the same kind of data. For example, in [4] 77 participants are recorded speaking isolated vowels, and the annotation is also made using self-reported scores from the KSS scale. Reported performances on two classes (S and NS) are around 78 % of correction identification.  In a more recent paper [5], the number of participants is increased (402), the recordings are read passages from 7 texts. However the classification task is not the same since the classifier tries to predict the value of the KSS score. In our project, in close partnership with the SANSPY unit, we started to record patients (current number of patients recorded is 78) while asssessing their sleepiness states by various measurements including EEG as well as clinical expertise. Recording the patients follows a strict clinical methodology resulting in sets of 4 recordings per patient, always at the same time of the day. Three categories of sleepiness level have been devised according to the health professionals (instead of usually two in previous research on sleepiness detection in speech): very sleepy, intermediate and normal. Using these recordings and the provided categories, we begun to test different features and classification methods. Using a relatively small set of features and simple classification techniques, we managed to obtain in a cross validation procedure a global classification rate of 70% correct. The task of the intern student is to further explore the different possibilities in terms of features and machine learning methods as the data collection continues, and to carry on thorough analysis of
the results so as to understand the influence of several factors such as  gender, age, or pathology.

References:

[1] Shahid, A., Wilkinson, K., Marcu, S., & Shapiro, C. M. (2011). Karolinska sleepiness scale (KSS). In STOP, THAT and One Hundred Other Sleep Scales (pp. 209-210). Springer New York.

[2] Schuller, B.; Steidl, S.; Batliner, A.; Schiel, F.; Krajewski, J.: “The Interspeech 2011 Speaker State Challenge”, Interspeech (2011), ISCA, Florence, Italy, 2011.

[3] Dong-Yan Huang, Zhengchen Zhang, Shuzhi Sam Ge, Speaker state classification based on fusion of asymmetric simple partial least squares (SIMPLS) and support vector machines, In Computer Speech & Language, Volume 28, Issue 2, 2014, Pages 392-419, ISSN 0885-2308, https://doi.org/10.1016/j.csl.2013.06.002.

[4] Krajewski, J., Schnieder, S., Sommer, D., Batliner, A., & Schuller, B. (2012). Applying multiple classifiers and non-linear dynamics features for detecting sleepiness from speech. Neurocomputing, 84, 65-75.

[5] Krajewski, J., Schnieder, S., Monschau, C., Titt, R., Sommer, D., & Golz, M. (2016, October). Large Sleepy Reading Corpus (LSRC): Applying Read Speech for Detecting Sleepiness. In Speech Communication; 12. ITG Symposium; Proceedings of (pp. 1-4). VDE.

Requested skills:

- speech processing and/or signal processing techniques

- machine learning

- programming languages : matlab, python, C/C++

- interest in clinical research and/or cognitive sciences

Back

Top

Post-doctoral positions at Telecom-ParisTech on Deep learning approaches for social computing in human-agent interactions

*Place of work* Telecom ParisTech [TPT] 46 rue Barrault  75013 Paris ? France Paris until september 2019, and then Palaiseau (Paris outskirt)

*Starting date* From now to September 2019

*Salary* according to background and from 2300 ? /month

*Duration* 12 months renewable

*Context*
The post-doctoral fellowship will take part in the Telecom-ParisTech?s chair on Data Science & Artificial Intelligence for Digitalized Industry & Services [DSIAI]. Established for a five-year period, one of its main goals of the chair is to allow sustainable funding of research activities in AI and Data Science, on methodological topics crucial for applications.
The research activity of the postdoctoral fellowship will also contribute to the Social Computing topic [SocComp.] of the S2a team [SSA] at Telecom-ParisTech, in close collaboration with other researchers and PhD students of the team.

* Candidate profile*
As a minimum requirement, the successful candidate should have:
? A PhD in one or more of the following areas: human-agent interaction, deep learning, computational linguistics, affective computing, reinforcement learning, natural language processing, speech processing
?    Excellent programming skills (preferably in Python)
?    Excellent command of English

*How to apply*
The application should be formatted as **a single pdf file** and should include:
?    A complete and detailed curriculum vitae
?    A letter of motivation
?    The defense and Phd reports
?    The contact of two referees
The pdf file should be sent to the two supervisors: Chloé Clavel [Clavel] and Giovanna Varni [Varni]: chloe.clavel@telecom-paristech.fr, giovanna.varni@telecom-paristech.fr


1/ First position: Multimodal attention models for predicting the user's socio-emotional behavior in human-machine interactions

*Keywords* human-machine interaction, attention models, recurrent neural networks, Social Computing, natural language processing, speech processing, multimodality

*Supervision* Chloé Clavel,  Giovanna Varni,

*Description* Social robotics, and more broadly human-agent interaction, is a field of human-machine interaction for which the integration of socio-emotional behaviors (emotions, social attitudes, personality) is expected to have a great potential. For example, companion robots are designed to provide their users with both help (especially in the assistance and dependency market) and entertainment (in the entertainment market). For intelligent cars, the analysis of the driver's emotions through multimodal sensors can provide a better understanding of his driving [CARS]
This post-doctoral fellowship will focus on multimodal modeling for the prediction of the user's socio-emotional behaviors during interactions with a virtual agent.   In particular, the post-doctoral fellow will address the following points:
- the encoding of multimodal representations relevant for the modelling of socio-emotional behavior;
- the development and evaluation of models that take advantage of the complementarity of modalities in order to monitor the evolution of the user's socio-emotional behaviors during the interaction (e. g. taking into account the inherent sequentially of the interaction structure)
The models will be based on sequential neural approaches (recurrent networks) that integrate attention models as a continuation of the work done in [Hemamou] and [BenYoussef].


2/ Second Position: Reinforcement learning for the development of socially competent agents

*Keywords* human-machine dialogue, reinforcement learning, language generation model, Social Computing

*Supervision* Chloé Clavel

*Description* Conversational agents (e.g. Djingo, Orange, Alexa d'Amazon, Siri d'Apple, Cortana de Microsoft, etc.), chatbots and more broadly human-agent interaction and social robotics (see for example [CIMON]) are applications for which the integration of socio-emotional behaviour analysis in the interaction between humans and virtual agents has great potential. Recent developments in artificial intelligence in natural language processing have made it possible to set up functional chatbots: extraction of keywords, understanding of natural language, question and answer systems, dialogue trees. While virtual assistants are already on the market, taking into account the social component of interaction remains a crucial issue for the fluidity and naturalness of interaction. For example, the development of socio-emotional interaction strategies can compensate for the chatbot's lack of understanding of user requests, which results in expressions of frustration and irritation on the part of the user [Maslowski] and can lead to the user abandoning the conversation (also called an engagement breakdown [BenYoussef]), thus hindering the completion of the chatbot's intended task.
This post-doctoral fellowship will address this issue - the development of socially competent agents - by proposing methods of reinforcement and deep learning [Qureshi, Ritschel] for the selection and generation of natural language utterances based on their socio-emotional relevance.

Selected references of the team:
[Hemamou] L. Hemamou, G. Felhi, V. Vandenbussche, J.-C. Martin, C. Clavel, HireNet: a Hierarchical Attention Model for the Automatic Analysis of Asynchronous Video Job Interviews.  in AAAI 2019, to appear
[Garcia] Alexandre Garcia, Chloé Clavel, Slim Essid , Florence d?Alche-Buc, Structured Output Learning with Abstention: Application to Accurate Opinion Prediction, ICML 2018
[Clavel&Callejas] Clavel, C.; Callejas, Z., Sentiment analysis: from opinion mining to human-agent interaction, Affective Computing, IEEE Transactions on, 7.1 (2016) 74-93.
[Langlet] C. Langlet and C. Clavel, Improving social relationships in face-to-face human-agent interactions: when the agent wants to know user?s likes and dislikes , in ACL 2015
[Maslowski]  Irina Maslowski, Delphine Lagarde, and Chloé Clavel.  In-the-wild chatbot corpus: from opinion analysis to interaction problem detection, ICNLSSP 2017.
[Ben-Youssef]  Atef Ben-Youssef, Chloé Clavel, Slim Essid, Miriam Bilac, Marine Chamoux, and Angelica Lim.  Ue-hri: a new dataset for the study of user engagement in spontaneous human-robot interactions.  In  Proceedings of the 19th ACM International Conference on Multimodal Interaction, pages 464?472. ACM, 2017.

Other references:
[DSIAI] https://datascienceandai.wp.imt.fr/
[TPT] https://www.telecom-paristech.fr/eng/ 
[SocComp.] https://www.tsi.telecom-paristech.fr/recherche/themes-de-recherche/analyse-automatique-des-donnees-sociales-social-computing/
[SSA] http://www.tsi.telecom-paristech.fr/ssa/#
[Clavel] https://clavel.wp.imt.fr/publications/
[Varni] https://sites.google.com/site/gvarnisite/
[CARS] https://www.lesfurets.com/assurance-auto/actualites/voiture-intelligente-decrypter-emotions
[CIMON] http://blogs.esa.int/alexander-gerst/2018/11/16/alexander-welcomes-cimon/
[Qureshi]  Ahmed Hussain Qureshi, Yutaka Nakamura, Yuichiro Yoshikawa, and Hiroshi Ishiguro.  Robot gains Social Intelligence through Multimodal Deep Reinforcement Learning. Humanoid Robots (Humanoids), 2016 IEEE-RAS 16th International Conference on. IEEE, 2016.
[Ritschel] Ritschel, Hannes, and Elisabeth André. 'Real-time robot personality adaptation based on reinforcement learning and social signals.' Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. ACM, 2017.

Back

Top

L'équipe SAMoVA de l'IRIT (Institut de Recherche en Informatique de Toulouse) recrute un chercheur ou une chercheuse en post-doctorat pour le projet collaboratif LinTo (PIA - Programme d?Investissements d'Avenir), projet d?assistant conversationnel destiné à opérer en contexte professionnel pour proposer des services en lien avec le déroulement de réunions.  

Ce travail post-doctoral concerne l?analyse du flux audio pour extraire un ensemble d?indicateurs non verbaux destinés à compléter la transcription automatique générée par d?autres partenaires du projet. Cet enrichissement aura pour rôle d?apporter des indications précieuses pour aider à la compréhension du déroulement des réunions, que ce soit au niveau des interactions, entre participants ou avec l?assistant vocal, ou de manière plus détaillée au niveau du contenu des échanges.

Plusieurs pistes de recherche pourront être explorées en fonction du profil de la personne recrutée ainsi des situations étudiées dans le cadre du projet :
- Analyse acoustique pour la recherche de marqueurs prosodique pertinents ;
- Exploration des approches de type Speech2Vect pour extraire des indicateurs plus marqués sémantiquement ;
- Application de méthodes d'apprentissage semi-supervisé dans un contexte faiblement annoté.

Informations Pratiques :
Poste à pourvoir : post-doc
Durée: 12-20 mois à partir de février/mars 2019
Domaine : analyse acoustique - traitement automatique de la parole -  apprentissage automatique - interaction conversationnelle
Lieu : Institut de Recherche en Informatique de Toulouse (Université Paul Sabatier) -  Equipe SAMoVA
Profil recherché : titulaire d'un doctorat en informatique, machine learning, traitement de l'audio.
Contact : Isabelle Ferrané (isabelle.ferrane@irit.fr) 
Dossier de candidature : à envoyer avant le 15 février 2019.
Détail de l'offre :  https://www.irit.fr/recherches/SAMOVA/pagejobs.html
Salaire : selon expérience

Back

Top

Le département informatique de l?UFR Sciences de l?université
Paris-Sud recrute un.e Maître de Conférences pour renforcer son équipe
pédagogique et continuer de développer la recherche au sein du LIMSI
sur les les thèmes du traitement automatique des langues et de la
parole.

Les recherches de la personne recrutée porteront en priorité sur le
développement de nouvelles méthodes en traitement automatique de la
parole, avec par exemple les thématiques suivantes: la caractérisation
du locuteur dans un contexte multimédia ; l?étude des dimensions
affectives des interactions sociales ; l?étude des systèmes de
traduction automatique et l?apprentissage artificiel ; l?étude des
systèmes de reconnaissance vocale. Le laboratoire est également ouvert
à des candidatures qui mettraient en avant d?autres thématiques
relatives au traitement automatique de la parole, ou plus largement à
l?ensemble du champ du traitement automatique des langues.

La personne recrutée pourra enseigner dans toutes les filières
relevant du département informatique de l'UFR Sciences d?Orsay, au
niveau Licence et Master (classique et en apprentissage). Elle pourra
enseigner dans ses domaines d?intérêt et dans un ou desles domaines de
l'informatique qui auront besoin d'être renforcés. La personne pourra
également dispenser une partie de ses enseignements en anglais, en
particulier dans le cadre de Masters internationaux.

Les candidat.e.s pourront obtenir des informations plus précises à
partir de la page suivante:

https://www.limsi.fr/fr/limsi-emplois/offres-de-postes-chercheurs-et-enseignants-chercheurs

Back

Top

Fully funded PhD and PostDoc positions are available at the Casa Paganini -
InfoMus Research Centre (www.casapaganini.org), DIBRIS-Dept. of Informatics,
Bioengineering, Robotics, and Systems Engineering, Polytechnic School,
University of Genoa, Italy. Each research position will have a specific
focus on the development of computational models, multimodal systems and
interfaces, research experiment and prototypes in one of the following
areas: (i) automated measurement, analysis, and prediction of full-body
non-verbal individual movement qualities and emotions; (ii) automated
measurement, analysis, and prediction of full-body non-verbal social signals
(synchronization, entrainment, leadership).
Accepted candidates will develop a research plan in the framework of the
4-year (2019-2022) Horizon 2020 European Project FET Proactive EnTimeMent
(https://entimement.dibris.unige.it/ ), and may be asked to participate in
joint activities with research partners in EnTimeMent, including possible
short residencies at EnTimeMent partners' sites.

Requirements
Candidates should ideally have the following profile:
+ Master's degree in Computer Science, Computer Engineering or related
disciplines;
+ Excellent technical and programming skills (Python, Java, C/C++);
+ Prior experience in at least one of the following fields: human computer
interaction, affective computing, motion capture and motion analysis,
multimodal interfaces, sound analysis and interactive sonification, computer
vision, machine learning;     
+ Ability to work independently, self-motivation, and ability to actively
contribute as a member of a multidisciplinary research team including
experts in computer science and engineering, movement science, cognitive
neuroscience, cognitive and motoric rehabilitation, performing arts;
+ Strong commitment to advancing the state-of-the-art research and
publishing in top research venues;
+ Excellent communication skills in English.
 
Applying
To apply, please email your application to: antonio.camurri@unige.it and
gualtiero.volpe@unige.it
 The application should consist of a single pdf file including:
+ cover letter expressing your interest in the position and your profile
relevance;
+ curriculum vitae showing academic records with tracks related to the
themes of the thesis;
+ list of publications (post-doc applications only);
+ transcript of marks according to M1-M2 profile or last 3 years of
engineering or related school (PhD applications only);
+ contact and recommendation letter of at least two university referents;

As a preliminary step, candidates will be invited for a Skype interview.
Candidates may also be invited to a fully funded short research internship
in our research team during summer 2019. To be finally enrolled, candidates
will need to pass a formal evaluation performed by a selection committee
University of Genova will appoint according to the Italian laws.
The envisioned starting date for the first selected PhD candidates is
November 2019. PostDoc starting date is negotiable.

Conditions of employment     
Hired on a fixed-term contract at University of Genoa, working full-time at
the Casa Paganini-InfoMus Research Centre of DIBRIS, University of Genoa,
with possible short internships at a research centre of an EnTimeMent
project partner.
Duration: three years for PhD students; 2-year contract for post-docs
(possible extensions available).

Further Information     
For any question or further details please send email to
antonio.camurri@unige.it and gualtiero.volpe@unige.it

The Casa Paganini-InfoMus Research Centre at DIBRIS, Polytechnic School,
University of Genoa, Italy
As art influences science and technology, science and technology can in turn
inspire art. Recognizing this mutually beneficial relationship, researchers
at the Casa Paganini-InfoMus Research Centre work to combine scientific
research in information and communications technology (ICT) with artistic
and humanistic research.
The mission of Casa Paganini - InfoMus consists of carrying out scientific
and technological research on human-centered computing where art and
humanistic culture are a fundamental source of inspiration. The research
team includes computer engineers and experts from the human sciences and the
arts.
Scientific and technological research includes: investigation and
development of computational models and of multimodal systems focusing on
non-verbal, full-body, expressive, emotional, and social behavior
(entrainment, leadership); sound and music computing; interactive
sonification; multimodal interactive systems and serious games for
rehabilitation, entertainment, sport, edutainment, museums and cultural
institutions; multimedia systems and services for the creative industry: ICT
for active music listening, interactive dance, theatre, cultural heritage,
user-centric media and mobile systems.
The Casa Paganini - InfoMus Research Centre coordinates and participates as
partner in many international projects on scientific and technological
research, education, and develops multimedia systems, platforms, and
applications for the creative industry and cultural institutions.
www.casapaganini.org                youtube.com/InfoMusLab

The EnTimeMent EU Horizon 2020 FET PROACTIVE project
EnTimeMent aims at a radical change in scientific research and enabling
technologies for human movement qualitative analysis, entrainment and
prediction, based on a novel neuro-cognitive approach of the multiple,
mutually interactive time scales characterizing human behaviour. Our
approach will afford the development of computational models for the
automated detection, measurement, and prediction of movement qualities from
behavioural signals, based on multi-layer parallel processes at non-linearly
stratified temporal dimensions, and will radically transform technology for
human movement analysis. EnTimeMent new innovative scientifically-grounded
and time-adaptive technologies operate at multiple time scales in a
multi-layered approach: motion capture and movement analysis systems will be
endowed with a completely novel functionality, achieving a novel generation
of time-aware multisensory motion perception and prediction systems. The
proposed model and technologies will be iteratively tested and refined, by
designing and performing controlled and ecological experiments, ranging from
action prediction in a controlled laboratory setting, to prediction in
dyadic and small group interaction. EnTimeMent scenarios include health
(healing and support of everyday life of persons with chronic pain and
disability), performing arts (e.g. dance), sports, and entertainment group
activities, with and without living architectures. EnTimeMent will create
and support community-building and exploitation with concrete initiatives,
including a community of users and stakeholders, innovation hubs and SME
incubators, as premises for the consolidation beyond the end of the project
in a broader range of market areas.
http://entimement.dibris.unige.it

Back

Top

Fully funded PhD position at Graz University of Technology, Austria

Graz University of Technolgy (TU Graz) is the organizer of the INTERSPEECH 2019 conference in September 2019 and offers a PhD position in its Signal Processing and Speech Communication Laboratory. Be part of this exciting opportunity and join our team!

The position is for up to four years and involves both research and teaching commitments. Teaching will be focussed on problem classes and lab courses for fundamental subjects such as signal processing. Research will address interdisciplinary topics at the interface between automatic speech recognition and speech science. You will work on top-level publications and your PhD thesis under the joint supervision of Prof. Gernot Kubin and Dr. Barbara Schuppler. Graz University of Technology offers systematic guidance to their doctoral students in specific doctoral schools with structured programs, international cooperation opportunities, and more. All doctoral programs and more than half of our Masters' programs are taught in English. The gross salary (before taxes) for this full-time position is according to scale B1 at Austrian Universities, approximately 40.000,- EUR per year. The expected starting date is March-April 2019.

Mandatory skills of the candidates are a relevant master's degree in electrical or information engineering, computer science, or speech science; excellent programming skills; English language competence (IELTS 7.0 or higher). Expertise in signal processing and machine learning as well as knowledge of the German language are considered additional assets.

Interested candidates should send the following information in PDF format to Prof. Gernot Kubin (g.kubin@ieee.org): curriculum vitae, transcript of records of both Bachelor's and Master's degree courses, master's thesis and all publications, proof of English language competence, and contact information for 2 referees. Additional application documents may be required in due course. Female students are particularly encouraged to apply. For more information consult

https://www.spsc.tugraz.at  for the Signal Processing and Speech Communication Laboratory,
https://www.interspeech2019.org  for the INTERSPEECH 2019 conference,
https://www.tugraz.at/en/go/welcome-center  for information on student life at Graz University of Technology and in the city of Graz, Austria.

Back

Top

Faculty position (Associate professor) at Telecom ParisTech in 

Machine-Learning.

Important Dates 

?    May 3, 2019: closing date

?    June 3, 2019: hearings of preselected candidates

Telecom ParisTech?s [1] machine learning, statistics and signal processing group (a.k.a S²A group) [2], within the laboratoire de traitement et communication de l?information (LTCI) [5], is inviting applications for a permanent (indefinite tenure) faculty position at the *Associate Professor* level (Maitre de Conferences) in *Machine learning*.

Main missions

The recruit will be expected to:

Research activities

?    Develop groundbreaking research in the field of theoretical or applied machine learning,   targeting applications that are well aligned with the topics of the S²A group [3] and the Images, Data & Signals department [4], which include (and is not restricted to) sequential/reinforcement learning, multitask learning,  learning for structured data (e.g. time series analysis, audio signals), natural language processing, social signal processing, predictive maintenance, biomedical or physiological signal analysis, recommendation, finance, health, ?.  

?    Develop both academic and industrial collaborations on the same topic, including collaborative activities with other Telecom ParisTech research departments and teams, and research contracts with industrial players

?    Set up research grants and take part in national and international collaborative research projects

Teaching activities 

?    Participate in teaching activities at Telecom ParisTech and its partner academic institutions (as part of joint Master programs), especially in machine learning and Data science, including life-long training programs (e.g. the local Data Scientist certificate) 

Impact

?    Publish high quality research work in leading journals and conferences

?    Be an active member of the research community (serving in scientific committees and boards, organizing seminars, workshops, special sessions...)

Candidate profile

As a minimum requirement, the successful candidate will have:

?    A PhD degree

?    A track record of research and publication in one or more of the following areas: machine learning, applied mathematics, signal processing,

?    Experience in teaching

?    Good command of English

The ideal candidate will also (optionally) have:

?    Experience in temporal data analysis problems (sequence prediction, multivariate time series, probabilistic graphical models, recurrent neural networks...)

NOTE:

The candidate does *not* need to speak French to apply, just to be willing to learn the language (teaching will be mostly given in English)

Other skills expected include: 

?    Capacity to work in a team and develop good relationships with colleagues and peers

?    Good writing and pedagogical skills

More about the position

?    Place of work: Paris until 2019, then Saclay (Paris outskirts)

?    For more information about being an Associate Professor at Telecom ParisTech, check [6] (in French)

 How to apply

Applications are to be sent by e-mail to: recrutement@telecom-paristech.fr

The application should include:

?    A complete and detailed curriculum vitae

?    A letter of motivation

?    A document detailing past activities of the candidate in teaching and research: the two types of activities will be described with the same level of detail and rigor.

?    The texts of the main publications

?    The names and addresses of two referees 

?    A short teaching project and a research project (maximum 3 pages)

Contacts :

Stephan Clémençon (Head of the S²A group)

Gaël Richard (Head of the IDS department)

[1] http://www.tsi.telecom-paristech.fr

[2] http://www.tsi.telecom-paristech.fr/ssa/ 

[3] http://www.tsi.telecom-paristech.fr/aao/en/  

[4] http://www.tsi.telecom-paristech.fr/en/
[5] https://www.ltci.telecom-paristech.fr/?lang=en
[6] http://www.telecom-paristech.fr/telecom-paristech/offres-emploi-stages-theses/recrute-enseignants-chercheurs.html 

Back

Top

PhD student “Morphology in spoken word recognition models”

Location: Radboud University, Nijmegen

Supervision: Louis ten Bosch, Mirjam Ernestus, and Ingo Plag

Starting date: September / October 2019

Duration: 4 Years (3 years, with possibility of extension of 1 year)

Salary: Around 1600 euros a month

The project is part of the project DMC: Dutch morphologically complex words: The role of morphology in speech production and comprehension of the Spoken Morphology: Phonetics and phonology of complex words DFG research unit FOR 2373. It is funded by the Deutsche Forschungsgemeinschaft.

See http://www.spoken-morphology.hhu.de/en.html

Requirements:

We are looking for candidates with

• Master degrees in Linguistics, Artificial Intelligence, or Social Sciences;

• Clear interest in speech and language;

• Expertise in quantitative research methods, preferably including large scale data analyses or computational modeling;

• Effective verbal and written communicative skills in English.

Application:

Please send your motivation letter and extensive resume (including grades for all university courses) to m.ernestus@let.ru.nl.

The deadline is 15 March 2019 or until the position is filled.

Questions:

l.tenbosch@let.ru.nl or m.ernestus@let.ru.nl

Abstract:

The PhD student will study what properties a computational model of auditory word recognition needs to have in order to well simulate human listeners’ processing of morphologically complex words. The computational models that will be considered are DIANA (e.g., ten Bosch et al., 2013, 2014, 2015) and Naïve Discriminative Learning (NDL, e.g., Arnold et al. 2017), since these two models represent very different types of processing and both can receive the speech signal, with morpho-acoustic cues, as their inputs. The human data to be simulated will include the BALDEY database (Ernestus & Cutler, 2015). The PhD student will produce a dissertation consisting of several publishable articles, preceded by a General Introduction and followed by a general discussion.

Back

Top

Research engineer or post-doc position in Natural Language Processing:
Introduction of semantic information in a speech recognition system


 Supervisors: Irina Illina, MdC, Dominique Fohr, CR CNRS

Team: Multispeech, LORIA-INRIA (https://team.inria.fr/multispeech/)

Contact: illina@loria.fr, dominique.fohr@loria.fr

Duration: 12-15 months

Deadline to apply : March 15th, 2019

Required skills: Strong background in mathematics, machine learning (DNN),
statistics, natural language processing and computer program skills (Perl,
Python).

Following profiles are welcome, either:

·         Strong background in signal processing
or
·         Strong experience with natural language processing

Excellent English writing and speaking skills are required in any case.

Candidates should email a detailed CV with diploma

LORIA is the French acronym for the ?Lorraine Research Laboratory in Computer
Science and its Applications? and is a research unit (UMR 7503), common to
CNRS, the University of Lorraine and INRIA. This unit was officially created in
1997. Loria?s missions mainly deal with fundamental and applied research in
computer sciences.

MULTISPEECH is a joint research team between the Université of Lorraine, Inria,
and CNRS. Its research focuses on speech processing, with particular emphasis
to multisource (source separation, robust speech recognition), multilingual
(computer assisted language learning), and multimodal aspects (audiovisual
synthesis).

 Context and objectives

Under noisy conditions, audio acquisition is one of the toughest challenges to
have a successful automatic speech recognition (ASR). Much of the success
relies on the ability to attenuate ambient noise in the signal and to take it
into account in the acoustic model used by the ASR. Our DNN (Deep Neural
Network) denoising system and our approach to exploiting uncertainties have
shown their combined effectiveness against noisy speech.

The ASR stage will be supplemented by a semantic analysis. Predictive
representations using continuous vectors have been shown to capture the
semantic characteristics of words and their context, and to overcome
representations based on counting words. Semantic analysis will be performed by
combining predictive representations using continuous vectors and uncertainty
on denoising. This combination will be done by the rescoring component. All our
models will be based on the powerful technologies of DNN.

The performances of the various modules will be evaluated on artificially noisy
speech signals and on real noisy data. At the end, a demonstrator, integrating
all the modules, will be set up.

Main activities

 ? study and implementation of a noisy speech enhancement module and a
 propagation of uncertainty module;
 ? design a semantic analysis module;
 ? design a module taking into account the semantic and uncertainty information.

References

[Nathwani et al., 2018] Nathwani, K., Vincent, E., and Illina, I. DNN
uncertainty propagation using GMM-derived uncertainty features for noise robust
ASR, IEEE Signal Processing Letters, 2018.

[Nathwani et al., 2017] Nathwani, K., Vincent, E., and Illina, I. Consistent DNN
uncertainty training and decoding for robust ASR, in Proc. IEEE Automatic
Speech Recognition and Understanding Workshop, 2017.

[Nugraha et al., 2016] Nugraha, A., Liutkus, A., Vincent E. Multichannel audio
source separation with deep neural networks. IEEE/ACM Transactions on Audio,
Speech, and Language Processing, 2016.

[Sheikh, 2016] Sheikh, I. Exploitation du contexte sémantique pour améliorer la
reconnaissance des noms propres dans les documents audio diachroniques?, These
de doctorat en Informatique, Université de Lorraine, 2016.

[Sheikh et al., 2016] Sheikh, I. Illina, I. Fohr, D. Linares, G. Learning word
importance with the neural bag-of-words model, in Proc. ACL Representation
Learning for NLP (Repl4NLP) Workshop, Aug 2016.

[Mikolov et al., 2013a] Mikolov, T. Chen, K., Corrado, G., and Dean, J.
Efficient estimation of word representations in vector space, CoRR, vol.
abs/1301.3781, 2013.

Back

Top

 SHEFFIELD CENTRE FOR DOCTORAL TRAINING IN SPEECH AND LANGUAGE

The University of Sheffield has won an £8M grant to fund some 60 PhDs over a period of 8
years in Speech and Language Technology. This 'Centre for Doctoral Training' is one of 16
newly announced by UKRI (UK Research and Innovation) to strengthen British AI research.

The Sheffield CDT will cover a wide range of SLT subjects, researching and developing
robust methods for natural language and speech processing in challenging real world
scenarios. Its research will stimulate novel applications of SLTs in sectors such as
health, sport, manufacturing, aerospace, robotics, finance, political science and digital
humanities.

A major feature of the CDT is close collaboration with industry, involving multinationals
such as Google, Amazon, Voicebase, Nuance, NHS Digital, Solvay and TechNation, as well as
substantial UK SME support.

In addition to a Ph.D., students will complete a Postgraduate Diploma (PGDip) in SLT
Leadership. This bespoke programme will provide them with the necessary skills for
academic and industrial leadership in SLT, and will cover software engineering,
entrepreneurship, management, and societal responsibility.

The SLT is headed by Professor Thomas Hain and Professor Rob Gaizauskas.

For more details, visit

https://www.sheffield.ac.uk/dcs/latest-news/speech-and-language-centre-for-doctoral-training-1.831895

https://slt-cdt.group.shef.ac.uk/

Back

Top

En collaboration avec les directions des trois plateformes technologiques1 CRVM, CEP et H2C2, et sous la responsabilité du chargé de mission Plateformes technologiques d?Aix-Marseille, le/la chargé.e d?affaires conçoit et met en oeuvre la stratégie de développement des

offres de services des plateformes technologiques afin de favoriser leur croissance.

 Date limite de candidature 23 avril 2019

Back

Top

    
CRIM (COMPUTER RESEARCH INSTITUTE OF MONTREAL) IS HIRING! 

POSTDOCTORAL RESEARCHER POSITION - SPEAKER RECOGNITION Speech and Text group
 
CRIM (http://www.crim.ca/en) is an applied research and expertise centre in information technology, dedicated to making organizations more effective and competitive through the development of innovative technology and the transfer of leading edge know-how, while contributing to scientific advancement.
 
JOB DESCRIPTION
CRIM is looking for a postdoctoral researcher with a background in speaker recognition, and, ideally, in other related fields such as speaker diarization, speech recognition and machine learning. The successful candidate will work on speaker recognition RD activities within the Speech and Text group.
 
The ideal candidate must be able to work on team research themes and supervise graduate students in an open environment where collaborations with experts in other fields at CRIM is valued.
 
The position is offered on a one-year basis with the possibility of renewal for up to 3 or 4 years depending on performance and funding.
 
Responsibilities:

● Perform high quality research on speaker recognition and anti-spoofing

● Assist in supervising graduate students

● Publish in referred journals and conferences

● Write/contribute to grant applications for new research projects

SKILLS AND EXPERIENCE
Required qualifications: 

● Doctoral degree (Ph.D.) in a relevant field

● Exceptional academic record and a clear aptitude for research ● Experience in student supervision ● Good publication record

● Excellent verbal and written communication skills in English
 
 
Preferred qualifications:

● Familiarity with modern distributed programming environments and with languages such as C++, Python and Perl

● Programming experience with relevant tools such as Theano, TensorFlow, Torch or Kaldi

● Proficiency in written and spoken French 
 
 
ABOUT THE ENVIRONMENT 
Located in Montreal, an historical, vibrant and culturally diverse city with 6 universities, recognized for its safety and low cost of living. Already a favorite of high tech and creative industries, Montreal have recently received large public and private investments and been turned into a leading center in machine learning and artificial intelligence research. 

GOOD REASONS FOR JOINING CRIM’S TEAM
● Benefit from various attractive employment conditions (Drug and health insurance plan, Pension plan, Competitive salary, French training programs)

● Reap the benefits of an outstanding work atmosphere, characterized by mutual support and good humour 

● Work alongside passionate people in a collaborative setting 

● Maintain work/family balance and quality of life 

HOW TO APPLY
Apply directly to emploi@crim.ca, or use our online form.
 
CRIM is an equal opportunity employer and values diversity. We encourage the development of ideas as a team and cultivate an open work environment that respects differences. We encourage all candidates to apply for this position; however, only selected individuals will be contacted. Thank you for your interest in CRIM!
 
Join CRIM’s team and work with dynamic and passionate people!

Back

Top

Professor (W2) Speech Technology and Hearing Devices at University of
Oldenburg, Cluster of Excellence Hearing4all
Oldenburg, Germany (website: http://hearing4all.eu/EN/)

-> link: https://uol.de/stellen/?stelle=66194

Back

Top

Title: PhD positions at the University of Edinburgh

Link: http://www.edinburghnlp.org/cdt/

Text:

FULLY FUNDED FOUR-YEAR PHD STUDENTSHIPS

UKRI CENTRE FOR DOCTORAL TRAINING IN NATURAL LANGUAGE PROCESSING

School of Informatics
School of Philosophy, Psychology and Language Sciences
University of Edinburgh

UK Research and Innovation has recently announced funding for a Centre
in Doctoral Training in Natural Language Processing (CDT in NLP) at
the University of Edinburgh. This CDT offers unique, tailored doctoral
training consisting of both taught courses and a doctoral
dissertation. Both components run concurrently over four years. Each
student will take a set of courses designed to complement their existing
expertise and give them an interdisciplinary perspective on NLP. They
will received full funding for four years, plus generous
funding for travel, equipment, and research costs.

The CDT brings together researchers in NLP, speech, linguistics, cognitive
science, and design informatics from across the University of Edinburgh.
Students will be supervised by a team of over 40 world-class faculty
and will benefit from cutting edge computing and experimental facilities,
including a large GPU cluster and eye-tracking, speech, virtual reality,
and visualization labs. The CDT involves over 20 industrial partners,
including Amazon, Facebook, Huawei, Microsoft, Mozilla, Reuters, Toshiba,
and the BBC. Close links also exist with the Alan Turing Institute and the
Bayes Centre.

The first cohort of CDT students will start in September 2019, and we
are now seeking applications. A wide range of research topics fall
within the remit of the CDT:

• Natural language processing and computational linguistics

• Speech technology

• Dialogue, multimodal interaction, language and vision

• Information retrieval and visualisation, computational social science

• Computational models of human cognition and behaviour, including
  language and speech processing

• Human-Computer interaction, design informatics, assistive and
  educational technology

• Psycholinguistics, language acquisition, language evolution,
  language variation and change

• Linguistic foundations of language and speech processing
  Approximately 8 studentships are available, covering both maintenance
  at the research council rate of GBP 15,009 per year and tuition fees.
  Studentships are available for UK, EU, and non-EU nationals.

Applicants should have an undergraduate or master?s degree in computer
science, linguistics, cognitive science, AI, or a related discipline. We
particularly encourage applications from women, minorities, and members
of other groups that are underrepresented in technology.

Further details including the application procedure can be found at:

http://www.edinburghnlp.org/cdt/

In order to ensure full consideration for funding, applications
(including all supporting documents) need to be received by 29 March
2019. Please direct inquiries to the PhD admissions team at
cdt-nlp-admissions@inf.ed.ac.uk.

The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.

Back

Top

Postdoctoral researcher

Job description
CONTEXT IRISA (https://www.irisa.fr/) is the largest research laboratory dedicated to computer science in France, hosting more than 800 people and 40 research teams. Its activities spans all the fields of computer science. It is located in Rennes, Lannion, and Vannes. The Expression team (https://www-expression.irisa.fr/) focuses on natural language processing (NLP), be it through texts, speech or gestures. In particular, it has internationally recognized expertise in textto-speech (TTS). The opened position is part of a project aimed at the synthesis of the Breton language. The recruited person will collaborate with other researchers and engineers implied in NLP and TTS.
TASKS Development of NLP modules for Breton and integration in a TTS pipeline, i.e.:

1. Phonetization, grapheme-to-phoneme conversion.

2. Text normalization.

3. POS tagging and chunking ;

4. Adaptation of the team’s TTS engine. The position also includes data management and project monitoring tasks.
REQUIRED QUALIFICATION & SKILLS

• PhD in computer science

• Recent skills in natural language processing

• Recent skills in machine learning

• Top academic and publication records

• Good communication skills

• Team work experience

CONTRACT

• 18 month, full time.

• Campus of Lannion (22).

• Competitive salary, depending on the experience.

• Starting as soon as possible.

APPLICATION & INFORMATION

Please send a cover letter and resume to :

• Gwénolé Lecorvé, gwenole.lecorve@irisa.fr

• Damien Lolive, damien.lolive@irisa.fr

Application deadline: 31th March 2019.

Back

Top

Two PhD positions in the area of Information Extraction, Data Mining and Machine Learning at Saarland University

We anticipate the availability of funds for two PhD positions in the area of information extraction, data mining and machine learning.

The project aims at designing a framework for extracting evidence and actionable intelligence from large amount of noisy multilingual multimodal data based on advanced speech and language technologies (SLTs), visual analysis (VA) and network analysis (NA). The overall project goal is to achieve a significant improvement in identification of events, entities and relations, and to design a new generation of probabilistic and neural networks based tools interfacing SLT, VA and NA technologies. The research will be carried out together with a European consortium of high-profile research institutes and companies.

The successful candidate should have a degree in computer science, computational linguistics, mathematics, physics or a discipline with a related background. Excellent programming skills in modern object-oriented languages are required, as well as strong analytical and problem-solving skills. Relevant expertise in the area of the project is desired. Very good oral and written communication skills in English are required.
This work will be conducted at the Spoken Language Systems group (http://www.lsv.uni-saarland.de/) at Saarland University.
Saarland University (http://www.uni-saarland.de/en/) is a European leader in Computer Science research and teaching, and is particularly well-known for its research in Computational Linguistics and Natural Language Processing. In addition, the Max Planck Institute for Computer Science, the Max Planck Institute for Software Systems and the German Research Center for Artificial Intelligence (DFKI) are located on campus. Students and researchers come from many countries and the research language is English.

The department of Language Science and Technology at Saarland University is one of the leading departments in the speech and language area in Europe. The flagship project at the moment is the CRC on Information Density and Linguistic Encoding. It also runs a significant number of European and nationally funded projects.
Both positions are 3-years positions with a salary German TV-L E13 scale (75%). The starting salary is about 34,000 euros per year and increases with experience.  
Each application should include:

* Curriculum Vitae including a list of publications (if applicable)
* Transcript of records
* Short statement of interest (not more than half a page)
* Names of two references
* Any other supporting information or documents

Applications (documents in PDF format in a single file) should be sent to: Dietrich.Klakow@lsv.uni-saarland.de
Priority will be given to applications received by Monday April 15th 2019.
Further inquiries regarding the project should be directed to: Michael A. Hedderich or Olga Petukhova

Back

Top

L'équipe Analyse-Synthèse de l'IRCAM recherche un/une chargé(e) de recherche pour le
développement des technologies relatives à l?analyse automatique d?enregistrements
musicaux (progression d?accords, identification d?instruments, qualité audio,
auto-tagging).

EXPÉRIENCE ET COMPÉTENCES REQUISES :

- Titulaire d?un doctorat, le/la candidat/e a une très bonne connaissance du traitement
du signal (analyse spectrale, extraction de descripteurs audio, estimation de paramètres)
ainsi qu?en algorithmes d?apprentissage automatique (SVM, ConvNet) et en calcul distribué
;
- Il/Elle maîtrise la programmation Matlab, Python et C/C++ ;
- Il/Elle a une bonne connaissance de l'environnement UNIX (GNU-Linux ou MacOSX) ;
- Haute productivité, travail méthodique, excellent style de programmation, bonne
communication, rigueur.

Toutes autres information sont disponibles à cette adresse :
  https://www.ircam.fr/job-offer/chargee-de-recherche-3/

Prière d'envoyer une lettre de motivation avec la référence 201903UMGRES et un CV
détaillant le niveau d'expérience/expertise dans les domaines mentionnés ci-dessus (ainsi
que toute autre information pertinente) à mignot at ircam dot fr avec copie à vinet at
ircam dot fr et roebel at ircam dot fr.

Back

Top

Faculty position (Associate professor) at Telecom ParisTech in 

Machine-Learning.

Important Dates 

?    May 3, 2019: closing date

?    June 3, 2019: hearings of preselected candidates

Telecom ParisTech?s [1] machine learning, statistics and signal processing group (a.k.a S²A group) [2], within the laboratoire de traitement et communication de l?information (LTCI) [5], is inviting applications for a permanent (indefinite tenure) faculty position at the *Associate Professor* level (Maitre de Conferences) in *Machine learning*.

Main missions

The recruit will be expected to:

Research activities

?    Develop groundbreaking research in the field of theoretical or applied machine learning,   targeting applications that are well aligned with the topics of the S²A group [3] and the Images, Data & Signals department [4], which include (and is not restricted to) sequential/reinforcement learning, multitask learning,  learning for structured data (e.g. time series analysis, audio signals), natural language processing, social signal processing, predictive maintenance, biomedical or physiological signal analysis, recommendation, finance, health, ?.  

?    Develop both academic and industrial collaborations on the same topic, including collaborative activities with other Telecom ParisTech research departments and teams, and research contracts with industrial players

?    Set up research grants and take part in national and international collaborative research projects

Teaching activities 

?    Participate in teaching activities at Telecom ParisTech and its partner academic institutions (as part of joint Master programs), especially in machine learning and Data science, including life-long training programs (e.g. the local Data Scientist certificate) 

Impact

?    Publish high quality research work in leading journals and conferences

?    Be an active member of the research community (serving in scientific committees and boards, organizing seminars, workshops, special sessions...)

Candidate profile

As a minimum requirement, the successful candidate will have:

?    A PhD degree

?    A track record of research and publication in one or more of the following areas: machine learning, applied mathematics, signal processing,

?    Experience in teaching

?    Good command of English

The ideal candidate will also (optionally) have:

?    Experience in temporal data analysis problems (sequence prediction, multivariate time series, probabilistic graphical models, recurrent neural networks...)

NOTE:

The candidate does *not* need to speak French to apply, just to be willing to learn the language (teaching will be mostly given in English)

Other skills expected include: 

?    Capacity to work in a team and develop good relationships with colleagues and peers

?    Good writing and pedagogical skills

More about the position

?    Place of work: Paris until 2019, then Saclay (Paris outskirts)

?    For more information about being an Associate Professor at Telecom ParisTech, check [6] (in French)

 How to apply

Applications are to be sent by e-mail to: recrutement@telecom-paristech.fr

The application should include:

?    A complete and detailed curriculum vitae

?    A letter of motivation

?    A document detailing past activities of the candidate in teaching and research: the two types of activities will be described with the same level of detail and rigor.

?    The texts of the main publications

?    The names and addresses of two referees 

?    A short teaching project and a research project (maximum 3 pages)

Contacts :

Stephan Clémençon (Head of the S²A group)

Gaël Richard (Head of the IDS department)

[1] http://www.tsi.telecom-paristech.fr

[2] http://www.tsi.telecom-paristech.fr/ssa/ 

[3] http://www.tsi.telecom-paristech.fr/aao/en/  

[4] http://www.tsi.telecom-paristech.fr/en/
[5] https://www.ltci.telecom-paristech.fr/?lang=en
[6] http://www.telecom-paristech.fr/telecom-paristech/offres-emploi-stages-theses/recrute-enseignants-chercheurs.html 

Back

Top

-- ************************ Professor Phil Green SPandH Dept of Computer Science University of Sheffield ************************ 

Back

Top

Senior Speech Processing Engineer (M/F) 

CONTEXT

Within the software department of Parrot Faurecia Automotive in Paris, you will be in charge of developing the speech recognition architecture on our new platform, managing the expertise topics related to Virtual Personal Assistants and managing specific developments for demonstrations and prototypes.

MISSIONS

Reporting to the manager of the company's transversal activities (voice recognition, navigation, translations, App Market), your missions will be as follows:

• Define, specify and implement the speech recognition architecture taking into account the limits and interests of both modes (embedded/deported)
• Prototype and model the different dialogue platforms
• Write the technical specifications of these HMIs
• Study, analyze and respond to calls for tenders related to the subject
• Participate in customer and supplier workshops
• Develop and implement the speech recognition and Natural Language Understanding (NLU) part for modeling and prototyping
• Identify and document speech recognition technological innovations to extract added value for the company
• Study, analyze and characterize speech and NLU recognition solutions from suppliers and competitors
• Collaborate within a dynamic and motivated team
• Ensure that speech recognition works properly on the new platform
• Create dialogue platforms that combine flexibility of use, robustness and performance
• Provide expertise and support on the theme 'Speech Processing': advice, methods, tools

PROFILE

You are a graduate of an engineering school or thesis, ideally in computer science with a specialization in speech processing.

You have at least 5 years of experience in the field of speech recognition. 

A good command of C++ and Python languages and a solid knowledge of speech processing are essential.

Ideally, you are familiar with:

• Understanding natural language
• Signal processing
• Nuance Technologies
• Google Assistant
• Alexa 
• Java development under Android 

Occasional travel is expected.

Fluent French is required.

Your level of English allows you to exchange in writing and orally, you master the technical vocabulary in particular.

Skills in Chinese would be a plus.

To apply you can send your resume directly to this email : mathilde.tarente@faurecia.com  or go to our website and apply by clicking on the link under the corresponding job offer : https://parrot-faurecia-automotive.talentview.io/jobs/7lz0hr?source=site_entreprise

We will get back to you shortly !!

• Annonce pour le poste de Voice Assistant Integration Engineer (M/F) :

Would you like to be part of a French company known for its innovation and agility? 

Come and join Parrot Faurecia Automotive, a leading automotive equipment manufacturer with a start-up mentality, working to develop the cockpit of the future!

Located in central Paris, our teams engineer innovative technologies by developing man-machine interfaces based on Android Auto to provide an intuitive connected experience to end users. Our embedded electronic solutions are equipped with powerful multi-core graphics processors to provide quick response times for multiple simultaneous instructions.

Join us in taking up one of the greatest challenges of the automotive industry!

We are looking for a Voice Assistant Integration Engineer (M/F) 

CONTEXT

Within the software department of Parrot Faurecia Automotive in Paris, you will be in charge of managing the expertise subjects related to Virtual Personal Assistants and managing specific developments for demonstrations and prototypes.

MISSIONS

Reporting to the manager of the company's transversal activities (voice recognition, navigation, translations, App Market), your missions will be as follows:

• Prototype and model the different dialogue platforms
• Write the technical specifications of these HMIs
• Study, analyze and respond to calls for tenders related to the subject
• Participate in customer and supplier workshops
• Develop and implement the speech recognition and Natural Language Understanding (NLU) part for modeling and prototyping
• Identify and document speech recognition technological innovations to extract added value for the company
• Study, analyze and characterize speech and NLU recognition solutions from suppliers and competitors
• Collaborate within a dynamic and motivated team

PROFILE

You are a graduate of an engineering school or thesis, ideally in computer science with a specialization in speech processing.

You have at least 2 years of experience in the field of speech recognition.

You are proficient in C++ language, Java development under Android and you have a solid knowledge of Google and Alexa assistants.

Ideally, you are familiar with:

• Understanding natural language
• Speech processing
• Signal processing
• Nuance Technologies
• The Python language

Occasional travel is expected.

Fluent French is required.

Your level of English allows you to exchange in writing and orally, you master the technical vocabulary in particular.

Skills in Chinese would be a plus.

To apply you can send your resume directly to this email : mathilde.tarente@faurecia.com  or go to our website and apply by clicking on the link under the corresponding job offer : https://parrot-faurecia-automotive.talentview.io/jobs/zxjk2o?source=site_entreprise

Back

Top

The European Language resources Distribution Agency (ELDA), a company specialised in Human Language Technologies within an international context is currently seeking to fill an immediate vacancy for a permanent Project Manager Intellectual Property and Language Data position.

Under the CEO?s supervision, the Project Manager Intellectual Property and Language Data will handle legal issues related to compilation, use and distribution of language datasets on a European and international scale. This yields excellent opportunities for young, creative, and motivated candidates wishing to participate actively to the Language Engineering field.

His main tasks will consist of:

• drafting and negotiating distribution contracts for language datasets to be added to an online catalogue;
• analysing of the legal status of language datasets;
• implementing GDPR requirements in the processing of language data;
• supervising data collection, particularly in the context of public Open Data;
• implementing evaluation procedures for IPR clearance of digital data.

A successful candidate:

• holds a Master?s degree (or equivalent) in IT Law, with good understanding of intellectual property and data protection;
• holds a Bachelor?s degree (or justifies equivalent experience) in Information Science, Knowledge Management or a similar domain;
• speaks fluent English, with advanced writing and analytical skills;
• is familiar with public licensing schemes (CC, GPL, etc.);
• justifies experience in project management and/or participation in European or international projects;
• is dynamic, communicative, flexible and willing to work on various tasks;
• is capable of working independently as well as in a team;
• is an EU citizen, or has a residence permit enabling him to work in France.

All applications will be carefully examined until the position is filled. The position is based in Paris.

Gross annual salary: 30.000-36.000 EUR depending on experience.

Applicants should email a cover letter addressing the points listed above together with a curriculum vitae to: job@elda.org. 

ELDA is a human-sized company (15 people) acting as the distribution agency of the European Language Resources Association (ELRA). ELRA was established in February 1995, with the support of the European Commission, to promote the development and exploitation of Language Resources (LRs). Language Resources include all data necessary for language engineering, such as monolingual and multilingual lexica, text corpora, speech databases and terminology. The role of this non-profit membership Association is to promote the production of LRs, to collect and to validate them and, foremost, make them available to users. The association also gathers information on market needs and trends.

For further information about ELDA/ELRA, visit: http://www.elra.info

Back

Top

Post-doc/PhD position Pattern mining for Neural Networks debugging: application to speech recognition

Back

Top

PhD Title: Remote and Automatic Monitoring of Bird Populations

Studentship: Full Scholarship, including fees (EU/Non EU) plus annual stipend of €18,000.

Start Date: Sept 2019

PhD Supervisor: Dr. Naomi Harte, Sigmedia Group, Electronic & Electrical Engineering, Trinity College Dublin, Ireland

Background:

The analysis of birdsong has increased in the speech processing community in the past 5 years. Much of the reported research has concentrated on the identification of bird species from their songs or calls. Birdlife International has identified over 12,000 sites globally that are home to bird species of conservation concern and other forms of biodiversity. Out of these, 422 are in danger due to a number of threats including human encroachment and climate change. One of the main challenges in properly managing these sites is adequately monitoring them to determine their state, threats to the ecosystem and responses to these threats. Remote monitoring is the best potential option to achieve the level of coverage required.

The objective of this PhD project is to define the next-generation approaches to the use of remote monitoring for populations of birds of conservation concern. This PhD programme will develop acoustic techniques for the monitoring of bird species of conservation concern by leveraging recent developments in speech and language processing technologies. The PhD will develop appropriate approaches to acoustic data collection in the wild to ensure that acoustic surveys yield accurate bird population data and investigate audio signal analysis steps necessary to extract useful information from these long recordings. In particular the student will focus on signal enhancement to mitigate noise, and the idea of diarisation, i.e. the concept of 'who sang when'. This ambitious approach will take concepts from speaker diarisation in the speech processing domain and attempt to make sense of recordings overall. Birdsong presents significant challenges beyond speech, with more rapid pitch fluctuations coupled with noisier recordings in the wild. Thus the research is very far from a re-application of knowledge from one domain to another. Also, rather than trying to identify specific species in a recording from a closed set of possible birds, this approach will consider an unconstrained set to add to the technical challenges and make the results even more impactful. The desire is to exploit online archives of birdsong recordings from experts such as those available on xeno-canto.org and The Macaulay Library at Cornell. Based on the known geographical location of a recording, spontaneous models of bird vocalisations from populations in that area could be constructed using machine learning performed on available archived recordings. Techniques developed originally for speaker

identification will be further developed for this application. This work will also leverage deep learning to quickly build accurate models from these large datasets.

Envisaged Outputs of the Research:

• • Signal processing algorithms to address noise issues specific to remote recordings in bird habitats.
• • Exploitation of advanced machine learning approaches, including deep learning, to identify portions of recordings that contain bird activity.
• • Disruptive approaches to automatic bird species identification to leverage opensource repositories to identify birds present in the recordings.

Requirements:

The ideal candidate for this position will:

• • Have a primary degree (first class honours) in Electrical Engineering, Computer Engineering or a closely related discipline.
• • Possess strong written and oral communication skills in English.
• • Have a strong background and interest in digital signal processing (DSP)
• • Have strong coding skills
• • Be mathematically minded, and be curious about nature.

Interested candidates should send an email to Prof. Naomi Harte at nharte@tcd.ie. The email MUST include the following:

• • Candidate CV (max 2 pages)
• • A short statement of motivation (half page)
• • Scanned academic transcripts
• • Proof of English language competency (where applicable, see 1)
• • Name and contact details for TWO academic referees

1 https://www.tcd.ie/study/international/how-to-apply/entry-requirements.php#language-requirements

Incomplete applications may not be considered.

Back

Top

Two PhD Positions in Computational Linguistics or Phonetics or Speech Science

We are pleased to announce the availability of two PhD positions in the Language Science
and Technology department at Saarland University in a project funded by the German
Research Foundation (DFG). The three-year project is entitled 'Pause-internal phonetic
particles' and is directed by Jürgen Trouvain and Bernd Möbius. Its focus is on the
production and perception of vocalisations such as breath noises and tongue clicks
typically found in speech pauses. Research in the project will be based on corpus
analyses and production and perception experiments and develop pause models for speech
synthesis.

The successful candidates should have a Master's degree in Computational Linguistics or
Phonetics or Speech Science, or a related discipline. A good command of English is
mandatory. Working knowledge of German is desirable but not a prerequisite. Candidates
must have completed their Master studies by the time of the appointment. We are happy to
consider applicants who have not yet finished their MA/MSc by the time of application but
will have submitted their thesis by the starting date. Both contracts are funded for
three years at a 65% salary on the German TV-L 13 scale.

The doctoral researchers will join a vibrant community of speech and language researchers
at Saarland University whose expertise spans areas such as computational linguistics,
psycho-linguistics, language and speech technology, speech science, theoretical and
corpus linguistics, computer science, and psychology. Saarland University offers a lively
academic environment for phonetics research. The department of Language Science and
Technology is one of the leading departments in speech and language in Europe, with
approximately 50 postdoctoral researchers and PhD students. The flagship project at the
moment is the Collaborative Research Centre on Information Density and Linguistic
Encoding. It also runs a significant number of European and nationally funded projects.

Applicants with a degree in Phonetics, Computational Linguistics, Spoken Language
Processing, Speech Technology, or related fields, are encouraged to submit a full CV
(including transcripts and copies of certificates, as well as two references) and a
sample of written academic work, accompanied by a cover letter, to
trouvain[at]coli.uni-saarland.de and moebius[at]coli.uni-saarland.de. Review of
applications will begin on June 3, 2019, and will continue until the suitable candidates
are found.

Dr. Jürgen Trouvain and Prof. Bernd Möbius

--
Jürgen Trouvain
Saarland University
Language Science and Technology
Campus C7.2
D-66123 Saarbrücken
Tel.: +49 - (0)681 - 302 - 46 94

Back

Top

The European Language resources Distribution Agency (ELDA), a company specialised in Human Language Technologies within an international context is currently seeking to fill an immediate vacancy for a permanent Technical Engineer/Scientist (Project Manager) position, specialized in Speech and Multimodal technologies.

Technical Engineer / Scientist (Project Manager) in Speech and Multimodal Technologies

Under the supervision of the CEO, the responsibilities of the Technical Engineer/Scientist include designing/specifying language resources, setting up production frameworks and platforms, carrying out quality control and assessment. He/she will be in charge of renovating the current language resources production workflows. This yields excellent opportunities for young, creative, and motivated candidates wishing to participate actively to the Language Engineering field. He/she will be in charge of conducting the activities related to language resources and Speech and Multimodal technologies. The task will mostly consist in managing language resources production projects and co-ordinating ELDA?s participation in R&D projects while being also hands-on whenever required by the development team.

Profile :

• PhD in computer science, speech, audiovisual/multimodal technologies
• Experience and/or good knowledge in speech data collection, expertise in phonetics, transcription tools
• Experience in speech recognition, synthesis, speaker ID and the well-used packages (e.g. KALDI) and the tools to produce, collect and assess quality of resources and datasets
• Experience and/or good knowledge of the Language Technology area
• Experience with technology transfer projects, industrial projects, collaborative projects within the European Commission or other international frameworks
• Good knowledge of Linux and open source software
• Proficiency in Python
• Hands-on experience in Django is a plus
• Ability to work independently and as part of a team, in particular the ability to supervise members of a multidisciplinary team
• Dynamic and communicative, flexible to combine and work on different tasks
• Proficiency in French and English
• Citizenship of (or residency papers) a European Union country

All applications will be carefully examined until the position is filled. The position is based in Paris.

Salary: Commensurate with qualifications and experience.

Applicants should email a cover letter addressing the points listed above together with a curriculum vitae to: job@elda.org. 

ELDA is a human-sized company (15 people) acting as the distribution agency of the European Language Resources Association (ELRA). ELRA was established in February 1995, with the support of the European Commission, to promote the development and exploitation of Language Resources (LRs). Language Resources include all data necessary for language engineering, such as monolingual and multilingual lexica, text corpora, speech databases and terminology. The role of this non-profit membership Association is to promote the production of LRs, to collect and to validate them and, foremost, make them available to users. The association also gathers information on market needs and trends.

For further information about ELDA/ELRA, visit: http://www.elra.info

Back

Top

Dans le cadre du projet national ANR VocADom, le Laboratoire d?Informatique de Grenoble
(LIG) recrute un ingénieur Développeur Domotique/Traitement multimédia temps-réel.
L?objectif général du projet de recherche industrielle VocADom vise à définir, en lien
avec les utilisateurs finaux, les fonctionnalités d?un système domotique à commande
vocale s?adaptant à l?utilisateur et utilisable au domicile dans des conditions réelles
(bruit, présence de plusieurs personnes). Plus détails peuvent être trouvé sur le site du
projet VocADom (https://vocadom.imag.fr).

*Mission :* La mission consiste à adapter le middleware OpenHAB gérant les capteurs et
les actionneurs de l?appartement intelligent du LIG et d?un mini-kit domotique portable
aux spécifications du projet et à réaliser l?interfaçage avec les outils de traitements
automatiques développés par les partenaires du projet. Par ailleurs, l?ingénieur sera
chargé de la mise en place technique des expérimentations et de leurs scénarios, incluant
la production et la synchronisation des données multimédia (vidéo, audio, traces
domotiques). Enfin, il sera soutien de l?intégration des algorithmes temps-réel des
équipes de recherche dans l?architecture temps-réel développée par l?entreprise THEORIS,
partenaire du projet. Le travail proposé comprendra les tâches suivantes :
* prise en main et adaptation de l?infrastructure domotique OpenHab de l?appartement
intelligent du LIG ;
* conception du mini-réseau domotique portable (sur la base d?une architecture existante)
;
* soutien aux expérimentations du projet ;
* soutien à l?intégration des algorithmes temps-réels des équipes de recherche ;
* documentation.

*Profil et compétences souhaitées :*

* niveau ingénieur ou M2 en informatique,
* compétences opérationnelles en génie logiciel (gestion de version, tests, qualité de
code),
* connaissance de Java, OSGi, C, C++, Linux,
* autonomie et force de proposition, capacité de gestion de projet,
* une première expérience d?utilisation des middlewares domotiques (OpenHAB, UPnP,
KNX...) serait un plus.

*Salaire :* 1650 à 1950e net/mois selon l?expérience

*Environnement de travail :* Le poste sera rattaché au Laboratoire d?Informatique de
Grenoble, UMR CNRS, au sein de l?équipe GETALP. L?équipe GETALP (http://getalp.imag.fr/)
regroupe plus de 40 chercheurs, ingénieurs et étudiants dans le domaine du traitement
automatisé des langues et de la parole multilingue. Le candidat sera également amené à
collaborer étroitement avec l?équipe IIHM du LIG et avec les collaborateurs de la Maison
de l?Innovation et de la Création (Maci).

*Candidature* Envoyer un CV, une lettre de motivation accompagnée éventuellement de 1 à 3
lettres de recommandation à Michel.Vacher@imag.fr et Francois.Portet@imag.fr. Les
candidatures seront examinées dès à présent au fil de l?eau jusqu?à la date de démarrage.
Merci de candidater dès que possible avant cette date.

Back

Top