ISCA - International Speech
Communication Association


ISCApad Archive  »  2022  »  ISCApad #287  »  Jobs

ISCApad #287

Monday, May 09, 2022 by Chris Wellekens

6 Jobs
6-1(2021-12-18) Master or PhD internships

Hi,


You are in a Master or PhD program (in NLP or Speech proc.) and want to do an internship in 2022 co-supervised by and
This offer is for you ! https://tinyurl.com/intern-nle-sb (You can apply online from the Web link)
—————

Joint ASR and Repunctuation for Better Machine and Human Readable Transcripts 

 
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6-2(2021-12-19) 2 research engineer positions ALAIA, IRIT, Toulouse, France

Afin de renforcer son équipe, le laboratoire Commun ALAIA, destiné à l'Apprentissage des langues Assisté par Intelligence Artificielle, propose deux postes d'ingénieurs de recherche (12 mois).

ALAIA est centré sur l'expression et la compréhension orale d'une langue étrangère cible (L2). En collaboration avec ses deux partenaires, académique (IRIT) et industriel (Archean Technologie) ainsi que des experts en didactiques de langues, les missions consisteront à concevoir, développer et intégrer des services innovants basés sur l'analyse des productions des apprenants L2, la détection et la caractérisation d'erreurs allant du niveau phonétique au niveau linguistique. Elles seront affinées en fonction du profil des personnes recrutées.

Les compétences attendues portent sur le traitement automatique de la parole et du langage ainsi que les méthodes de machine learning. 

Les candidatures sont à adresser à Isabelle Ferrané (isabelle.ferrane@irit.fr) et Lionel Fontan (lfontan@archean.tech). N'hésitez pas à nous contacter pour de plus amples informations. 

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6-3(2021-12-26) Research associate and postdoc at Heriot-Watt University, Edinburgh, UK

1) Research Associate in Safe Conversational AI(re-advertising)

 
Closing date: 9th January 2022
 
?
 
We seek a candidate with experience in neural approaches to natural language generation, or closely related fields, including Vision + Language tasks.
 
Applicants interested in social computing tasks, such as online abuse detection and mitigation, as well as interdisciplinary candidates with a wider interest in ethical and social implications of NLP are also encouraged to apply.
 
The opportunity:
 
This is an exciting opportunity to work with a team developing safer AI methods bringing together AI researchers and researchers working on formal verification methods to researchers working on computational law. You will contribute your insight and experience into researching and developing deep learning methods for Conversational AI and closely related areas.
 
The project is led by Heriot-Watt University and in cooperation with the University of Edinburgh and Strathclyde, see https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/T026952/1
 
 
2) Postdoctoral Research Assistant in children's perceptions of technology
 
Closing date: 6th January 2022

?
 
We are looking for a creative and self-motivated researcher to investigate children?s knowledge and perceptions of conversational agents such as Alexa. The position is located at the University of Edinburgh's Moray House School of Education.
 
The opportunity:
 
This is an exciting opportunity to work with an interdisciplinary team of computer scientists and social psychologists at three Scottish universities on a project to address gender bias in conversational agents. You will contribute your insight and experience into researching technology with and for children to the team.
 
The project is led by Heriot-Watt University and in cooperation with the University of Edinburgh and Strathclyde, see https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/T024771/1
 
For any enquiries, please get in touch!
Prof. Verena Rieser 
Heriot-Watt University, Edinburgh
https://sites.google.com/site/verenateresarieser/
 
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6-4(2022-01-10) Conference coordinator, ISCA.

The International Speech Communication (ISCA) [www.isca-speech.org)

seeks application for a :

 

Conference Coordinator (f/m/d)

 

For a limited-term contract with 18h/week, with the perspective of extension towards an unlimited-term contract.

 

The International Speech Communication Association (ISCA) is a scientific non-profit organization according to the French law 1901. The purpose of the association is to promote, in an international world-wide context, activities and exchanges in all fields related to speech communication science and technology. The association is aimed at all persons and institutions interested in fundamental research and technological development that aims at describing, explaining and reproducing the various aspects of human communication by speech, e.g. phonetics, linguistics, computer speech recognition and synthesis, speech compression, speaker recognition, aids to medical diagnosis of voice pathologies.

 

One of the core activities of ISCA is to ensure the continuous organization of its flagship conference, Interspeech. The conference is organized each year in a different country by a different team; it typically attracts 1500 or more participants from all over the world. The role of this newly-created position of conference coordinator is to ensure a smooth organization of the conference over the years, according to well-established standards, and taking into account the aims ISCA has with the conference.

 

The role requires – amongst others – to take over the lead for the following activities:

  • Support in the organization and set-up of the Technical Program
  • Operation and maintenance of the central electronic review system
  • Maintenance of the reviewer database
  • Support in the organization of the Technical Program Committee meeting
  • Maintenance of paper templates and author/presenter instructions
  • Support for the production of proceedings
  • Communication with the organizing team and possible Professional Conference Organizers about all matters of the conference organization, such as planning of calls, committees, meeting space, technical equipment and tools, session planning, event planning, etc.
  • Communication with the ISCA board about all matters of the conference
  • Communication with the ISCA Administrative Manager about membership matters, meetings, etc.
  • Outbound communication via web, social media, etc.

 

Required competences:

 

We are looking for a self-motivated person who is enthusiastic about the organization of international scientific events, and has excellent organizational and communication skills (mostly in English). The person does not need to have a scientific background in speech communication and technology, but should be able to understand the scientific background, as well as the aims ISCA has with the organization of Interspeech conferences. A proven expertise in the organization of large-scale events is a must, and of scientific events is a plus.

 

The job can be carried out remotely from any location. A flexible allocation of time over the year is required, depending on the status of preparations (the conference is typically organized in September, and there is an expected increase in activity from March to September). The willingness to physically attend preparatory meetings and the conference is required.

 

Deadline : 15 March 2022.

 

 

 

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6-5(2022-01-28) ASSISTANT OR ASSOCIATE PROFESSOR IN SPEECH AND LANGUAGE TECHNOLOGY (tenure track) at Aalto University, Finland

Aalto opens a call for an assistant or associate professor in speech and language technology. 

https://aalto.wd3.myworkdayjobs.com/PrivateJobPosting/job/Otaniemi-Espoo-Finland/ASSISTANT-OR-ASSOCIATE-PROFESSOR-IN-SPEECH-AND-LANGUAGE-TECHNOLOGY--tenure-track-_R32437

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6-6(2022-02-01) Two post-docs at ADAPT, Dublin, Irland
We are looking to recruit two Post-Doctoral Researchers to join the UCD team in the ADAPT Research Centre (www.adaptcentre.ie).  These projects are part of the ADAPT Digital Content Transformation Strand.
 
1) Harnessing Speech for Social Inclusion
Rather than focusing on accuracy and error rates, evaluation of speech recognition systems should be contextualized with respect to how well they perform in situations where the interlocutor is not the ‘typical’ native speaker (e.g. a senior citizen, a citizen with disabilities or a non-native speaker). Through publicly engaged research, data will be collected on how well existing speech technology is serving our citizens. A systematic evaluation of the output data produced by existing ASR systems and the interactions that arise when an ASR ‘converses’ with a range of users will enable a categorization of interaction issues and error patterns that need to be accounted for when developing applications which provide interfaces to essential services. This project will involve the development of a system which facilitates diagnostic evaluation of ASRs in a variety of interaction scenarios providing linguistic cues the augmentation of the ASR and building on the low-resource MT technologies being developed in other ADAPT projects.
 
2) Embedding of Multi-level Speech Representations
While deep learning has led to huge performance gains in speech recognition and synthesis, only recently more focus is being placed on what deep learning may be able uncover about the patterns which humans use intuitively when interacting via speech and which distinguish native from non-native speakers. Such patterns are typically the focus of speech perception and experimental phonetic studies. This project aims to build on the notion of multi-linear or multi-tiered representations of speech, creating embeddings of multiple (sub- word) levels of representation – phonetic features, phonemes, syllable pieces and syllables – enabling a closer investigation of systematicity and variability of speech patterns. This research will find application in non-native speech recognition, in speech adaptation/accommodation for native and non-native interactions and in pronunciation training scenarios.
 
ADAPT is the world-leading SFI research centre for AI Driven Digital Content Technology, coordinated by Trinity College Dublin and based within Dublin City University, University College Dublin, Technological University Dublin, Maynooth University, Munster Technological University, Athlone Institute of Technology, and the National University of Ireland Galway. ADAPT’s research vision is to pioneer new forms of proactive, scalable, and integrated AI-driven Digital Content Technology that empower individuals and society to engage in digital experiences with control, inclusion, and accountability with the long-term goal of a balanced digital society by 2030. ADAPT is pioneering new Human Centric AI techniques and technologies including personalisation, natural language processing, data analytics, intelligent machine translation human-computer interaction, as well as setting the standards for data governance, privacy and ethics for digital content.

ADAPT Digital Content Transformation Strand
From the algorithmic perspective, new machine learning techniques will both enable more users to engage meaningfully with the increasing volumes of content globally in a more measurably effective manner, while ensuring the widest linguistic and cultural inclusion. It will enhance effective, robust integrated machine learning algorithms needed to provide multimodal content experiences with new levels of accuracy, multilingualism and explainability.
 
Full details of the positions, requirements and link to submit applications can be found at:
 
 
The closing date is 17:00hrs (local Irish time) on Monday 14th February 2022 and and candidates must apply via https://www.ucd.ie/workatucd/jobs/  
 
The reference is 
 
1) 014028 for Harnessing Speech for Social Inclusion
2) 014079 for Embedding of Multi-level Speech Representations
 
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6-7(2022-02-02) Professeur(-e), Sorbonne Université, Paris, France
Un poste de Professeure / Professeur des Universités en Intelligence artificielle : théorie et applications, est à pourvoir à Sorbonne Université avec une affection recherche dans un des laboratoires : ISIR, LIB, LIMICS ou LIP6. 
 
Professeure / Professeur des Universités 
Section 27 – Informatique 
Profil : Intelligence artificielle : théorie et applications

Date limite des candidatures au poste : le 04 mars 2022 à 16h

La personne recrutée contribuera significativement aux enseignements de Licence d’informatique dont les besoins couvrent l’ensemble de la discipline (algorithmique, programmation notamment objet, concurrente, fonctionnelle, web, mathématiques discrètes, structures de données, système, architecture, réseaux, compilation, bases de données, etc.) ainsi qu’au master d’informatique, en particulier pour les parcours ANDROIDE, BIM ou DAC.

Recherche :
Le poste est ouvert à tous les domaines de l’IA et de ses applications. La personne retenue intégrera l’un des laboratoires : ISIR, LIB, LIMICS ou LIP6 selon ses thématiques de recherche, et/ou de projets impliquant plusieurs laboratoires d’accueil au sein de SCAI (Sorbonne Center for Artificial Intelligence). La professeure ou le professeur devra être capable de coordonner des programmes collaboratifs nationaux et internationaux. La participation de la candidate ou du candidat, dans le passé, à des projets multidisciplinaires sera appréciée.

Lien vers la fiche de poste : https://www.galaxie.enseignementsup-recherche.gouv.fr/ensup/ListesPostesPublies/FIDIS/0755890V/FOPC_0755890V_391.pdf
 
 
En vous remerciant d’avance pour votre aide dans le partage de cette offre.
Bien cordialement, 



 
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6-8(2022-02-11) 2 research fellowship grant for collaboration in research activities, Kore University of Enna - Enna (Sicily), Italy

A public selection procedure is called, based on qualifications and interview, for the assignment of n. 2 research fellowship grant for collaboration in research activities

 

Project main aim: Multidisciplinary Research on AI for Health.

Location: Kore University of Enna - Enna (Sicily), Italy

Funding Programme: Research Projects of National Relevance - PRIN 2017

 

Description: The project focuses on idiopathic Parkinson’s disease dysarthric speech, produced by speakers of two varieties of Italian that show different segmental (consonantal, vocalic) and prosodic characteristics. The project as a whole aims at: identifying phonetic features that impact on speech intelligibility and accuracy, separating variability due to dysarthria from features due to sociolinguistic variation, and developing perspectives and tools for clinical practice that take variation into account.

 

Duration: 12 months

 

Link to apply: https://unikore.it/index.php/it/contratti-di-ricerca/item/41282-d-p-n-33-2022-2-assegni-di-ricerca-presso-l-universita-degli-studi-di-enna-kore

 

Further information contact Prof. Sabato Marco Siniscalchi, E-mail: marco.siniscalchi-at-unikore.it

 

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6-9(2022-02-08) PhD position at Delft University, The Netherlands

Job description

 

One of the most pressing matters that holds back robots from taking on more tasks and reach a widespread deployment in society is their limited ability to understand human communication and take situation-appropriate actions. This PhD positions is dedicated to addressing this gap by developing the underlying data-driven models that enable a robot to engage with humans in a socially aware manner.

This position is specifically targeted at the development of an argumentative dialogue system for human-robot interaction.  The PhD candidate will explore how to fuse multimodal behaviour to infer a person's perspective. The candidate will use, and further develop, reinforcement learning techniques in order to drive the robot's argumentative strategy for deliberating topics of current social importance such as global warming or vaccination.

The ideal candidate will have a keen interest in speech technology and reinforcement learning. He or she has strong interactive system background will design and run the experiments to evaluate the created hybrid-AI models through human-robot interaction.

Topics of interest:

1) long-term human-robot interaction

2) affective computing

2) NLP&argument-mining

 

Requirements

 
  • MSc in Computer Science or related field.
  • At least 3 years of programming experience in python (java  or C++is a plus).
  • Motivation to meet deadlines.
  • Affinitty to design and social science research.
  • Interest in collaborating with colleagues from Industrial Design.
  • Willingness to teach and guide students.
  • The ability to work in a team, take initiative, be results oriented and systematic.

Click here to apply:
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6-10(2022-02-17) Postdoctoral position at INRIA, Bordeaux, France

Postdoctoral position in Speech Processing at INRIA, Bordeaux, France

 

Title: Glottal source inverse filtering for the analysis and classification of pathological speech

Keywords: Pathological speech processing, Glottal source estimation, Inverse filtering, Machine learning, Parkinsonian disorders, Respiratory diseases

Contact and Supervisor: Khalid Daoudi (khalid.daoudi@inria.fr)

INRIA team: GEOSTAT (geostat.bordeaux.inria.fr)

Duration: 13 months (could be extended)

Starting date: between 01/04/2022 and 01/06/2022 (depending on the candidate availability)

Application : via https://recrutement.inria.fr/public/classic/en/offres/2022-04481

Salary: 2653€/month (before taxes, net salary 2132€)

Profile: PhD thesis in signal/speech processing (or a solid post-thesis experience in the field) Required Knowledge and background: A solid knowledge in speech/signal processing; Basics of machine learning; Programming in Matlab and Python. Scientific research context During this century, there has been an ever increasing interest in the development of objective vocal biomarkers to assist in diagnosis and monitoring of neurodegenerative diseases and, recently, respiratory diseases because of the Covid-19 pandemic. The literature is now relatively rich in methods for objective analysis of dysarthria, a class of motor speech disorders [1], where most of the effort has been made on speech impaired by Parkinson’s disease. However, relatively few studies have addressed the challenging problem of discrimination between subgroups of Parkinsonian disorders which share similar clinical symptoms, particularly is early disease stages [2]. As for the analysis of speech impaired by respiratory diseases, the field is relatively new (with existing developments in very specialized areas) but is taking a great attention since the beginning of the pandemic. The speech production mechanism is essentially governed by five subsystems: respiratory, phonatory, articulatory, nasalic and prosodic. In the framework of pathological speech, the phonatory subsystem is the most studied one, usually using sustained phonation (prolonged vowels). Phonatory measurements are generally based on perturbations or/and cepstral features. Though these features are widely used and accepted, they are limited by the fact that the produced speech can be a product of some or all the other subsystems. The latter thus all contribute to the phonatory performance. An appealing way to bi-pass this problem is to try to extract the glottal source from speech in order to isolate the phonatory contribution. This framework is known as glottal source inverse filtering (GSIF) [3]. The primary objective of this proposal is to investigate GSIF methods in pathological speech impaired by dysarthria and respiratory deficit. The second objective is to use the resulting glottal parameterizations as inputs to basic machine learning algorithms in order to assist in the discrimination between subgroups of Parkinsonian disorders (Parkinson’s disease, Multiple-System Atrophy, Progressive Supranuclear Palsy) and in the monitoring of respiratory diseases (Covid-19, Asthma, COPD). Both objectives benefit from a rich dataset of speech and other biosignals recently collected in the framework of two clinical studies in partnership with university hospitals in Bordeaux and Toulouse (for Parkinsonian disorders) and in Paris (for respiratory diseases).

Work description GSIF consists in building a model to filter out the effect of the vocal tract and lips radiation from the recorded speech signal. This difficult problem, even in the case of healthy speech, becomes more challenging in the case of pathological speech. We will first investigate time-domain methods for the parameterization of the glottal excitation using glottal opening and closure instants. This implies the development of a robust technique to estimate these critical time-instants from dysarthric speech. We will then explore the alternative approach of learning a parametric model of the entire glottal flow. Finally, we will investigate frequency-domain methods to determine relationships between different spectral measures and the glottal source. These algorithmic developments will be evaluated and validated using a rich set of biosignals obtained from patients with Parkinsonian disorders and from healthy controls. The biosignals are electroglottography and aerodynamic measurements of oral and nasal airflow as well as intra-oral and sub-glottic pressure. After dysarthric speech GIFS analysis, we will study the adaptation/generalization to speech impaired by respiratory deficits. The developments will be evaluated using manual annotations, by an expert phonetician, of speech signals obtained from patients with respiratory deficit and from healthy controls. The second aspect of the work consists in manipulating machine learning algorithms (LDA, logistic regression, decision trees, SVM…) using standard tools (such as Scikit-Learn). The goal here will be to study the discriminative power of the resulting speech features/measures and their complementarity with other features related to different speech subsystems. The ultimate goal being to conceive robust algorithms to assist, first, in the discrimination between Parkinsonian disorders and, second, in the monitoring of respiratory deficit.

Work synergy - The postdoc will interact closely with an engineer who is developing an open-source software architecture dedicated to pathological speech processing. The validated algorithms will be implemented in this architecture by the engineer, under the co-supervision of the postdoc. - Giving the multidisciplinary nature of the proposal, the postdoc will interact with the clinicians participating in the two clinical studies.

References: [1] J. Duffy. Motor Speech Disorders Substrates, Differential Diagnosis, and Management. Elsevier, 2013. [2] J. Rusz et al. Speech disorders reflect differing pathophysiology in Parkinson's disease, progressive supranuclear palsy and multiple system atrophy. Journal of Neurology, 262(4), 2015. [3] P. Alku. Glottal inverse filtering analysis of human voice production – A review of estimation and parameterization methods of the glottal excitation and their applications. Sadhana – Academy Proceedings in Engineering Sciences. Vol. 36, Part 5, pp. 623-650, 2011

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6-11(2022-02-15) Thèse CIFRE L3i La Rochelle-EasyChain



La jeune compagnie EasyChain et le laboratoire de recherche L3i de La Rochelle lancent un appel à candidatures pour un poste de doctorant.e CIFRE dans le domaine de développement des agents conversationnels.

Les détails de l'offre sont disponibles à cette adresse :
Dynamic Human-Agent Interactions adapted to users? profiles.

Le.la candidat.e retenu.e devra être titulaire d'un master ou d'un diplôme équivalent en informatique ou en traitement automatique des langues. Un solide bagage en apprentissage automatique et une bonne communication en anglais sont requis.

La thèse se déroulera principalement à Niort chez EasyChain, dans un environnement francophone.

Si vous êtes intéressé.e par ce poste, veuillez envoyer les informations suivantes à Antoine Doucet (antoine.doucet@univ-lr.fr),  et Ahmed Hamdi (ahmed.hamdi@univ-lr.fr):
 * CV détaillé
 * Diplômes de licence et de master.
* Lettres de recommandation

Les candidatures seront étudiées jusqu'au 1 mars 2022.

Bien cordialement

Antoine Doucet

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6-12(2022-04-05) Junior professor position at Université du Mans, France
L?Université du Mans ouvre une Chaire Professeur Junior en traitement du langage multimodal.
 
Les candidatures sont ouvertes sur Galaxie à déposer avant le 2 mai 2022.
 

Projet de recherche / Description of the research project

L?objectif principal est de développer une IA de traitement du langage multimodale et multilingue qui repose sur un espace de représentation commun pour les modalités parole et texte dans différentes langues. Le ou la candidat.e devra développer ses activités de recherche afin de renforcer le caractère transverse de ces représentations à travers une combinaison pertinente de modalités (par ex. : vidéo et texte ou texte et parole), de tâches (par ex. : caractérisation du locuteur et synthèse de la parole, compréhension de la parole et traduction automatique, reconnaissance de la parole et synthèse de résumé?automatique) et de langues. Ses travaux de recherche tendront à?développer des systèmes automatiques intégrant l?humain au c?ur du traitement en utilisant des approches d?apprentissage actif et en explorant les problématiques d?expliquabilité?et d?interpretabilité afin de permettre à l?utilisateur naïf d?enseigner au système automatique ou d?en extraire des éléments compréhensibles. Ce projet visera également le renforcement de collaborations existantes (Facebook, Orange, Airbus) ou la création de nouveaux partenariats (Oracle, HuggingFace?). 

The research project should take place in the LST team goal that aims at developping a multimodal and multilingual representation space for speech and text modalities. The Junior Professor is expected to develop his/her own research diretions between the topics already existing in the LST team and to develop hybrid approaches by mixing for instance speaker characterization and speech synthesis or speech translation and speech understanding. He/She should also integrate the strategy of the team to involve the human in the loop for deep learning systems and work towards a better explainability/interpretability of speech processing algorithms.

Projet d'enseignement / Description of the teaching project 

Le ou la candidat.e intégrera l?équipe pédagogique du Master en intelligence artificielle du département d?informatique de l?UFR Sciences et Techniques de l?Université? du Mans. Son implication aura pour but de renforcer les compétences en apprentissage profond (apprentissage auto-supervisé, GANs, Transfomer, méthodologies et protocoles pour l?IA?) mais également dans les infrastructures dédiées à l?apprentissage automatique et aux sciences des données (calcul distribué, SLURM, MPI), l?utilisation d?un cluster de calcul (ssh, temux, jupyter-lab, conda) ou le cloud computing. Fort de compétences reconnues en traitement automatique du langage et de la parole l?équipe pédagogique souhaite élargir son offre de formation en adaptant les contenus à d?autres types de données (images, séquences temporelles générées par différents types de capteurs, graphes?) afin de répondre aux besoins spécifiques du tissu industriel local et régional en apprentissage automatique. Cette action s?inscrira dans la volonté de l?équipe pédagogique de développer l?apprentissage et la formation continue en lien avec les partenaires industriels mais également à destination d?un public académique de chercheurs et enseignant chercheurs non-informaticiens souhaitant développer des compétences en apprentissage automatique.

Teaching activities will take place within the Master of Computer Sciences and Artificial Intelligence from Le Mans University. The candidate is expected to strengthen the teaching on deep learning (self-supervised training, GANs, Transformers, machine learning methodology and protocols?) but also teach tools for distributed learning (SLURM, MPI, ssh, temux, jupyter-lab, conda?) and cloud computing. In mid terms, the candidate will contribute to the development of a  continuing learning in artificial intelligence adapted to the need of local companies and industry but also for researchers non-specialist in computer sciences. 

Conditions de candidature / Application requirements

être titulaire d'un doctorat  / hold a PhD

Pour les candidats exerçant ou ayant cessé d'exercer depuis moins de dix-huit mois une fonction l'enseignant-chercheur, d'un niveau équivalent à celui de l'emploi à pourvoir, dans un établissement d'enseignement supérieur d'un État autre que la France:  titres, travaux et tout élément permettant d'apprécier le niveau de fonction permettant d'accorder une dispense de doctorat. 

For candidates exercising or having ceased to exercise for less than eighteen months a function of teacher-researcher, of a level equivalent to that of the position to be filled, in a higher education establishment of a State other than France: titles, works and any element allowing to appreciate the level of function allowing to grant a dispence of doctorate.

Contact 

Antoine LAURENT

Antoine.laurent@univ-lemans.fr

Anthony LARCHER

Anthony.larcher@univ-lemans.fr

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6-13(2022-03-17) un poste de doctorant(e) dans le domaine de la détection multimodale de deep fakes, IRISA, Lannion, France
L?équipe EXPRESSION de l?IRISA lance un appel à candidatures pour un poste de doctorant ou doctorante dans le domaine de la détection multimodale de deep fakes.

Les détails de l'offre sont disponibles à cette adresse : 
MUDEEFA - MUltimodal DeEEp Fake detection using Text-To-Speech Synthesis, Voice Conversion and Lips Reading
 
Le candidat ou la candidate devra mener des recherches appliquées de pointe dans un ou plusieurs des domaines suivants : traitement du signal, apprentissage automatique statistique, reconnaissance de la parole et des gestes. Il/elle devra posséder d'excellentes compétences en programmation informatique (par exemple C/C++, Python/Perl, etc.), et des connaissances en apprentissage automatique, en traitement du signal ou en interaction homme-machine.
Le poste nécessite d'être titulaire d'un master en informatique ou d'un diplôme d'ingénieur donnant le titre de master en informatique.

La thèse se déroulera à Lannion, dans les Côtes d?Armor, au sein de l?équipe EXPRESSION.

Merci d'envoyer un CV détaillé, une lettre de motivation, une ou plusieurs lettres de référence et les résultats académiques du diplôme précédent (Master ou Ingénieur donnant le titre de Master) à tous les contacts indiqués dans le sujet avant le vendredi 8 avril 2022, limite stricte.

Bien cordialement

Arnaud Delhay-Lorrain

 

Arnaud Delhay-Lorrain - Associate Professor
IRISA - Université de Rennes 1 
IUT de Lannion - Département Informatique
Rue Edouard Branly - BP 30219
F-22 302 LANNION Cedex
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6-14(2022-03-18) 3 speech-to-speech translation positions available at Meta/Facebook FAIR
We are seeking research scientists, research engineers and postdoctoral researchers with expertise on speech translation and related fields to join our team.

FAIR?s mission is to advance the state-of-the-art in artificial intelligence through open research for the benefit of all. As part of this mission, our goal is to provide real-time, natural-sounding translations at near-human level quality. The technology we develop will enable multilingual live communication. We aim for our technology to be inclusive: it should support both written and unwritten languages. Finally, in order to preserve the authenticity of the original content, especially for more creativity related content, we aim to preserve non-lexical elements in the generated audio translations. Ideal candidates will have expertise on speech translation or related fields such as speech recognition, machine translation or speech synthesis. Please send email to juancarabina@fb.com with a CV if you are interested in applying.
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6-15(2022-03-21) Thèse ou postdoc at Laboratoire d'Informatique de Grenoble, France
Sujet de thèse ou de postdoctorat dans le cadre du projet  Popcorn (projet collaboratif avec deux entreprises)
encadrée par Benjamin Lecouteux, Gilles Sérasset et Didier Schwab (Laboratoire d?Informatique de Grenoble, Groupe d?Étude en Traduction Automatique/Traitement Automatisé des Langues et de la Parole)


Titre : Peuplement OPérationnel de bases de COnnaissances et Réseaux Neuronaux


Le projet aborde le problème de l?enrichissement semi-automatisé d?une base de connaissance au travers de l?analyse automatique de textes. Afin d?obtenir une innovation de rupture dans le domaine du Traitement Automatique du Langage Naturel (TALN) pour les clients sécurité et défense, le projet se focalise sur le traitement du français (même si les approches retenues seront par la suite généralisables à d?autres langues). Les travaux aborderont différents aspects :
? L?annotation automatique de documents textuels par la détection de mentions d?entités présentes dans la base de connaissance et leurs désambiguïsation sémantique (polysémie, homonymie) ;
? La découverte de nouvelles entités (personnes, organisations, équipements, événements, lieux), de leurs attributs (âge d?une personne, numéro de référence d?un équipement, etc.), et des relations entre entités (une personne travaille pour une organisation, des personnes impliquées dans un événement, ...). Une attention particulière sera donnée au fait de pouvoir s?adapter souplement à des évolutions de l?ontologie, la prise en compte de la place de l?utilisateur et de l?analyste pour la validation/capitalisation des extractions effectuées.
Le projet se focalise autour des trois axes de recherches suivants :
? Génération de données synthétiques textuelles à partir de textes de référence ;
? La reconnaissance des entités d?intérêt, des attributs associés et des relations entre les entités.
? La désambiguisation sémantique des entités (en cas d?homonymie par exemple)


Profil recherché:
    - Solide expérience en programmation & machine learning pour le Traitement Automatique de Langues (TAL), notamment l?apprentissage profond
    - Master/Doctorat Machine Learning ou informatique, une composante TAL ou linguistique computationnelle sera un plus apprécié
    - Bonne connaissance du français


Détails pratiques:
    - Début de la thèse rentrée 2022
    - Contrat doctoral à temps plein au LIG (équipe Getalp) pour 3 ans (salaire: min 1768? brut mensuel)
    - ou Contrat postdoctoral à temps plein au LIG (équipe Getalp) pour 20 mois (salaire: min 2395? brut mensuel)



 

Environnement scientifique:


  • Le doctorat ou le postdoctorat sera mené au sein de l'équipe Getalp du laboratoire LIG  (https://lig-getalp.imag.fr/).
  • La personne recrutée sera accueillie au sein de l?équipe qui offre un cadre de travail stimulant, multinational  et agréable. 
  • Les moyens pour mener à bien le (post)doctorat seront assurés tant en ce qui concerne les missions en France et à l?étranger qu?en ce qui concerne le matériel (ordinateur personnel, accès aux serveurs GPU du LIG, Grille de calcul Jean Zay du CNRS).


Comment postuler ?

 

  • Pour postuler sur une thèse de doctorat, les candidats doivent être titulaires d'un Master en informatique, en apprentissage machine ou en traitement automatique du langage naturel (obtenu avant le début du contrat doctoral, les étudiants actuellement en master 2 peuvent ainsi postuler).
  • Pour postuler sur un postdoctorat, les candidats doivent être titulaires d'une thèse de doctorat en informatique,  en apprentissage machine ou en traitement automatique du langage naturel (obtenu avant le début du contrat doctoral, les étudiants dont la soutenance est prévue avant fin septembre 2022 peuvent ainsi postuler).
  • Ils doivent avoir une bonne connaissance des méthodes d?apprentissage automatique et idéalement une expérience en collecte et gestion de corpus.
  • Ils doivent également avoir une bonne connaissance de la langue française. 
Les candidatures doivent contenir : CV + lettre/message de motivation + notes de master + lettre(s) de recommandations; et être adressées à Benjamin Lecouteux (benjamin.lecouteux@univ-grenoble-alpes.fr), Gilles Sérasset (gilles.serasset@univ-grenoble-alpes.fr) et Didier Schwab (Didier.Schwab@univ-grenoble-alpes.fr
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6-16(2022-04-04) PhD position at INRIA-LORIA, Nancy, France

2022-04676 - PhD Position F/M Nongaussian models for deep learning based audio signal processing Level of qualifications required :Graduate degree or equivalent Fonction : PhD Position Context The PhD student will join the Multispeech team of Inria,that is the largest French research group in the field of speech processing. He/she will benefit from the research environment and the expertise in audio signal processing and machine learning of the team, which includes many researchers, PhD students, post-docs, and software engineers working in this field. He/she will be supervised by Emmanuel Vincent (Senior Researcher, Inria), and Paul Magron (Researcher, Inria). Assignment Audio signal processing and machine listeningsystems have achieved considerable progress over the past years, notably thanks to the advent of deep learning. Such systems usually process a timefrequency representation of the data, such as a magnitude spectrogram, and model its structure using a deep neural network (DNN). Generally speaking, these systems implicitly rely on the local Gaussian model [1],that is an elementary statistical model for the data. Even though it is convenient to manipulate, this model builds upon several hypotheses which are limiting in practice: (i) circular symmetry, which boils down t o discarding the phase information (= the argument of the complex-valued time-frequency coefficients); (ii) independence of the coefficients, which ignores the inherent structure of audio signals (temporal dynamics, frequency dependencies); and (iii)Gaussian density, which is not observed in practice. Statistical audio signal modeling is an active research field. However, recent advances in this field are usually not leveraged in deep learning-based approaches, thus their potential is currently underexploited. Besides, some of these advances are not mature enough to be fully deployed yet. Therefore, the objective of this PhD is to design advanced statistical signal models for audio which overcome the limitations of the local Gaussian model, while combining them with DNN-based spectrogram modeling. The developed approaches will be applied to audio source separation and speech enhancement. Main activities The main objectives of the PhD student will be: 1. To develop structured statistical models for audio signals, which alleviate the limitations of the local Gaussian model. In particular, t he PhD student will focus on designing models by leveraging properties that originate from signal analysis, such as the temporal continuity [2] or the consistency of the representation [3], in order to favor interpretability and meaningfulness of the models. For instance, alpha-stable distributions have been exploited in audio for their robustness [4]. Anisotropic models are an interesting research direction since they overcome the circular symmetry assumption, while enabling an interpretable parametrization of the statistical moments [5]. Finally, a careful design of the covariance matrix allows for explicitly incorporating time and frequency dependencies [6]. 2. To combine these statistical models withDNNs. This raises several technical difficulties regarding the design of, e.g., the neural architecture, the loss function, and the inference algorithm. The student will exploit and adapt the formalism developed in Bayesian deep learning, notably the variational autoencoding framework [7], as well as the inference procedures developed in DNN-free nongaussian models [8]. 3. To validate experimentally these methods on realistic sound datasets. To that end, the PhD student will use public datasets such as LibriMix (speech) and MUSDB (music), which are reference datasets for source separation and speech enhancement. The PhD student will disseminate his/her research results in international peer-reviewed journals and conferences. In order to promote reproducible research, these publications will be self-archived at each step of the publication lifecycle, and accessible through open access repositories (e.g., arXiv, HAL). The code will be integrated to Asteroid, that is the reference soDware for source separation and speech enhancement developed by Multispeech. Bibliography [1] E. Vincent, M. Jafari, S. Abdallah, M. Plumbley, M. Davies,Probabilistic modeling paradigms for audio source separation, Machine Audition: Principles, Algorithms and Systems, p.162–185, 2010. [2] T. Virtanen, Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria, IEEE/ACM Transactions on Audio, Speech and Language Processing, Vol. 15, no. 3, pp.1066-1074, 2007. [3] J. Le Roux, N. Ono, S. Sagayama, Explicit consistency constraints for STFT spectrograms and their application to phase reconstruction, Proc. SAPA, 2008. [4] S. Leglaive, U. Şimşekli, A. Liutkus, R. Badeau and G. Richard,Alpha-stable multichannel audio source separation, Proc. IEEE ICASSP, 2017. [5] P. Magron, R. Badeau, B. David, Phase-dependent anisotropic Gaussian model for audio source separation, Proc. IEEE ICASSP, 2017. [6] M. Pariente, Implicit and explicit phase modeling in deep learning-based source separation, PhD thesis - Université de Lorraine, 2021. [7] L. Girin, S. Leglaive, X. Bie,J. Diard, T. Hueber, X. Alameda-Pineda,Dynamical variational autoencoders: A comprehensive review, Foundations and Trends in Machine Learning, vol. 15, no. 1-2, 2021. General Information Theme/Domain : Language, Speech and Audio Town/city : Villers lès Nancy Inria Center : CRI Nancy - Grand Est Starting date : 2022-10-01 Duration of contract : 3 years Deadline to apply : 2022-05-02 Contacts Inria Team : MULTISPEECH PhD Supervisor : Magron Paul / paul.magron@inria.fr About Inria Inria is the French national research institute dedicated to digital science and technology. It employs 2,600 people. Its 200 agile project teams, generally run jointly with academic partners, include more than 3,500 scientists and engineers working to meet the challenges of digital technology, oDen at the interface with other disciplines. The Institute also employs numerous talents in over forty different professions. 900 research support staff contribute to the preparation and development of scientific and entrepreneurial projects that have a worldwide impact. The keys to success Upload your complete application data. Applications will be assessed on a rolling basis, thus it is advised to apply as soon as possible. Instruction to apply Defence Security : This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment. Recruitment Policy : As part of its diversity policy, all Inria positions are accessible to people with disabilities. Warning : you must enter your e-mail address in order to save your application to Inria. Applications must be submitted online on the Inria website. Processing of applications sent from other channels is not guaranteed. [8] P. Magron, T. Virtanen, Complex ISNMF: a phase-aware model for monaural audio source separation, IEEE/ACM Transactions on Audio, Speech and Language Processing, Vol. 27, no. 1, pp. 20-31, 2019. Skills Master or engineering degree in computer science, data science, signal processing, or machine learning. Professional capacity in English (spoken, read, and written). Some programming experience in Python andin somedeep learning framework (e.g., PyTorch). Previous experience and/or interest for speech and audio processing is a plus. Benefits package Restauration subventionnée Transports publics remboursés partiellement Congés: 7 semaines de congés annuels + 10 jours de RTT (base temps plein) + possibilité d'autorisations d'absence exceptionnelle (ex : enfants malades, déménagement) Possibilité de télétravail (après 6 mois d'ancienneté) et aménagement du temps de travail Équipements professionnels à disposition (visioconférence, prêts de matériels informatiques, etc.) Prestations sociales, culturelles et sportives (Association de gestion des œuvres sociales d'Inria) Accès à la formation professionnelle Sécurité sociale Remuneration Salary: 1982€ gross/month for 1st and 2 year. 2085€ gross/month for 3rd year. Monthly salary after taxes : around 1594€ for 1st and 2 year. 1677€ for 3rd year

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6-17(2022-04-06) Ph.D. Thesis position and Post-doc position at Loria-INRIA, Nancy, France
Ph.D. Thesis position and Post-doc position at Loria-INRIA, Mutlispeech team, Nancy (France)
 
Multimodal automatic hate speech detection
 
https://jobs.inria.fr/public/classic/fr/offres/2022-04660
https://team.inria.fr/multispeech/fr/category/job-offers/
 
Hate speech expresses antisocial behavior. In many countries, online hate 
speech is punishable by the law. Manual analysis of such content and its moderation 
are impossible. An effective solution to this problem would be the automatic 
detection of hate comments. Until now, for hate speech detection, only text 
documents have been used. We would like to advance the knowledge about hate speech 
detection by exploring a new type of document: audio documents.
 
We would like to develop a new methodology to automatically detect hate speech, 
based on Machine Learning and Deep Neural Networks using text and audio.
 
Required Skills: The candidate should have theoretical and a moderate practical experience
with Deep Learning, including a good practice in Python and an understanding of deep
learning libraries like Pytorch. The knowledge of NLP or signal processing will be helpful. 
 
Supervisors:
Irina Illina, Associate Professor, HDR, Lorrain University
Dominique Fohr, Senior Researcher, CNRS
https://members.loria.fr/DFohr/    dominique.fohr@loria.fr
 
 
MULTISPEECH is a joint research team between the Université of Lorraine, Inria, 
and CNRS. It is part of department D4 “Natural language and knowledge processing” 
of LORIA. Its research focuses on speech processing, with particular emphasis to 
multisource (source separation, robust speech recognition), multilingual (computer 
assisted language learning), and multimodal aspects.


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6-18(2022-04-07) Postes de traducteur peul-français (H/F), ELDA, Paris, France
Contexte
ELDA (Evaluation and Language resources Distribution Agency, www.elda.org) a pour activités principales la distribution et la production de ressources linguistiques, ainsi que l’évaluation de technologies de la langue (traduction automatique, reconnaissance de la parole, etc.).

Dans le cadre de ses activités de production, ELDA offre plusieurs postes de traducteur peul-français (H/F) à temps plein ou partiel pour la constitution d’un corpus de traduction de la langue.

Mission
Il s’agira de traduire à partir des documents audios et de leurs transcriptions, du peul vers le français afin de fournir les données nécessaires au développement et à l'évaluation de technologies de la langue peul. Le travail sera effectué selon des conventions de traduction sur lesquelles les candidats seront formés.

Profil recherché
• Natif de la langue peul, plus précisément de Mâssina
• Une première expérience en traduction du peul vers le français est souhaitable
• Excellente maîtrise du français écrit (orthographe, grammaire, syntaxe) pour la traduction et la compréhension des conventions de traduction
• Bonne maîtrise de l’outil informatique
• Capacité à intégrer des règles (de traduction) et à les suivre scrupuleusement et avec constance
 
Durée
Temps plein ou mi-temps, pour une durée de 5 mois minimum

Salaire
Salaire mensuel brut: 1925€

Les candidatures (CV, lettre de motivation) doivent être adressées à lucille@elda.org



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6-19(2022-04-07) Postes de transcripteur peul-français (H/F), ELDA, Paris, France

Contexte
ELDA (Evaluation and Language resources Distribution Agency, www.elda.org) a pour activités principales la distribution et la production de ressources linguistiques, ainsi que l’évaluation de technologies de la langue (traduction automatique, reconnaissance de la parole, etc.).

Dans le cadre de ses activités de production, ELDA offre plusieurs postes de traducteur peul-français (H/F) à temps plein ou partiel pour la constitution d’un corpus de traduction de la langue.

Mission
Il s’agira de traduire à partir des documents audios et de leurs transcriptions, du peul vers le français afin de fournir les données nécessaires au développement et à l'évaluation de technologies de la langue peul. Le travail sera effectué selon des conventions de traduction sur lesquelles les candidats seront formés.

Profil recherché
• Natif de la langue peul, plus précisément de Mâssina
• Une première expérience en traduction du peul vers le français est souhaitable
• Excellente maîtrise du français écrit (orthographe, grammaire, syntaxe) pour la traduction et la compréhension des conventions de traduction
• Bonne maîtrise de l’outil informatique
• Capacité à intégrer des règles (de traduction) et à les suivre scrupuleusement et avec constance
 
Durée
Temps plein ou mi-temps, pour une durée de 5 mois minimum

Salaire
Salaire mensuel brut: 1604€

Les candidatures (CV, lettre de motivation) doivent être adressées à lucille@elda.org

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6-20(2022-04-07) Contrat doctoral au Collegium Musicæ de Sorbonne Université, Paris, France

Le Collegium Musicæ de Sorbonne Université propose un contrat doctoral sur le style vocal :

 Analyse par la Synthèse performative du style vocal - Porteurs de projet : Christophe d'Alessandro  et Céline  Chabot-Canet

Le propos de cette thèse est l’étude du style vocal par le paradigme d’analyse par la synthèse
performative. Ce sujet associe recherche musicologique sur le style vocal et la musicologie de la
performance, recherche musicale sur les instruments chanteurs, et recherche en informatique musicale
sur les nouvelles interfaces pour l’expression musicale et les synthétiseurs vocaux temps-réel.

des détails se trouvent ici (aller sur l'onglet Collegium Musicæ de la page) :

https://www.sorbonne-universite.fr/projets-proposes-en-2022-programme-instituts-et-initiatives

Contact:

Christophe d'Alessandro : christophe.dalessandro@sorbonne-universite.fr

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6-21(2022-04-08) Postdocs at IMT Atlantique, Brest, France

L'équipe RAMBO de IMT Atlantique, en collaboration avec Smart Macadam, recherche des candidats pour

deux postdocs de 18-24 mois basés à Nantes sur les sujets suivants:

1. Postdoctorat sur la reconnaissance de parole (Nantes & Brest, France)
https://institutminestelecom.recruitee.com/l/fr/o/postdoctorante-ou-postdoctorant-reconnaissance-

automatique-de-la-parole-intelligence-artificielle-cdd-18-mois

2. Postdoctorat sur la reconnaissance de sons (Nantes & Brest, France)
https://institutminestelecom.recruitee.com/l/fr/o/postdoctorante-ou-postdoctorant-reconnaissance-de-sons-

intelligence-artificielle-cdd-18-mois

Date limite pour soumettre votre candidature (actualisée): 30 avril 2022

Nous vous encourageons de postuler si vous êtes intéressés !
N'hésitez pas de me contacter si vous avez des questions concernant ces positions.

Cordialement,

Mihai ANDRIES

Enseignant-chercheur
Équipe RAMBO
IMT Atlantique
Brest, France
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6-22(2022-04-10) PhD thesis positions, LaBRI, Bordeaux, France

Vocal biomarkers collected through conversational agents for diagnosis assistance and follow-up of

sleep and mental disorders 

Location : Bordeaux, France

Supervisor : Jean-Luc Rouas, CR CNRS HDR, LaBRI
Co-supervisor : Pierre Philip, PU-PH, Sanpsy

Duration : 36 mois
Starting date  : 01/10/2022
Gross salary : 2 135,00 € / month

https://emploi.cnrs.fr/Offres/Doctorant/UMR5800-MAGHIN-017/Default.aspx

Detailed subject: 


The SANPSY and the Labri teams have demonstrated their ability to identify new vocal biomarkers to measure excessive daytime sleepiness both subjectively and objectively in patients suffering from sleep disorders [1]–[5]. SANPSY demonstrated the validity of autonomous numeric solutions (I.e. smartphone based virtual agents) to diagnose sleep/mental disorders in the general population [6]– [10]. We plan now to develop new virtual agents collecting biomarkers (i.e. from speech) in our healthy subjects and patients cohorts for diagnostic, treatment and follow-up (ADDICTAQUI, KANOPEE and AUTONOMHEALTH(PEPR) projects).

The PhD thesis project “Vocal biomarkers collected through conversational agents for diagnosis assistance and follow-up of sleep and mental disorders“ relies on 4 stages:
1) developing new virtual agents to collect vocal markers:
The objective is to design new scenarios targeting behavioral interventions to improve fatigue, mood and excessive daytime sleepiness. Moreover, the scenarios will be designed so that the agent interacts with the subject in order to engage a discussion (spontaneous speech). This will lead to more ecological conditions that should increase the acceptability.
2) switching from high-quality controlled recordings made at the hospital to in-the-field unsupervised recordings using smartphones.
Our current vocal biomarkers are defined using a reading task and using high-quality microphones. The new interaction scenarios from task 1) will lead us to record spontaneous speech with smartphone microphones. This task will tackle the differences in recording conditions and their impact on our feature extraction pipeline.
3) verifying the relevance of the existing vocal markers when used with the new data and propose new features that could be used as high-level biomarkers such as lexical, syntactic and semantic cues.
Our features will have to be adapted to consider the versatile nature of spontaneous discourse which is a completely different speaking style from read speech. Spontaneous speech will however provide additional cues that could be used as high-level biomarkers such as lexical, syntactic and semantic markers.
4) studying the sensitivity and specificity of the selected biomarkers on diagnostic and follow up of symptoms and disorders with respect to other medical measures.
This final part of the PhD project will be addressed jointly by LaBRI and SANPSY and includes the clinical validation of the proposed approaches.

References:

[1]  V. P. Martin, G. Chapouthier, M. Rieant, J.-L. Rouas, and P. Philip, ‘Using reading mistakes as features for sleepiness detection in speech’, in 10th international conference on speech prosody 2020, Tokyo, Japan, May 2020, pp. 985–989. [Online]. Available: https://hal.archives- ouvertes.fr/hal-02495149
[2]  V. P. Martin, J.-L. Rouas, J.-A. Micoulaud-Franchi, and P. Philip, ‘The objective and subjective sleepiness voice corpora’, in 12th edition of its language resources and evaluation conference., Marseille, France, May 2020, pp. 6525–6533. [Online]. Available: https://hal.archives- ouvertes.fr/hal-02489433
[3]  V. P. Martin, J.-L. Rouas, and P. Philip, ‘Détection de la somnolence dans la voix : nouveaux marqueurs et nouvelles stratégies’, Trait. Autom. Lang., vol. 61, no. 2, p. 24, 2020.
[4]  V. P. Martin, J.-L. Rouas, F. Boyer, and P. Philip, ‘Automatic Speech Recognition Systems Errors for Objective Sleepiness Detection Through Voice’, in Interspeech 2021, Aug. 2021, pp. 2476– 2480. doi: 10.21437/Interspeech.2021-291.
[5]  V. P. Martin, J.-L. Rouas, J.-A. Micoulaud-Franchi, P. Philip, and J. Krajewski, ‘How to Design a Relevant Corpus for Sleepiness Detection Through Voice?’, Front. Digit. Health, vol. 3, p. 124, 2021, doi: 10.3389/fdgth.2021.686068.
[6]  L. Dupuy, J.-A. Micoulaud-Franchi, and P. Philip, ‘Acceptance of virtual agents in a homecare context: Evaluation of excessive daytime sleepiness in apneic patients during interventions by continuous positive airway pressure (CPAP) providers’, J. Sleep Res., vol. n/a, no. n/a, p. e13094, 2020, doi: https://doi.org/10.1111/jsr.13094.
[7]  L. Dupuy et al., ‘Smartphone-based virtual agents and insomnia management: A proof-of-concept study for new methods of autonomous screening and management of insomnia symptoms in the general population’, J. Sleep Res., p. e13489, Sep. 2021, doi: 10.1111/jsr.13489.
[8]  P. Philip et al., ‘Trust and acceptance of a virtual psychiatric interview between embodied conversational agents and outpatients’, Npj Digit. Med., vol. 3, no. 1, Art. no. 1, Jan. 2020, doi: 10.1038/s41746-019-0213-y.
[9]  P. Philip et al., ‘Virtual human as a new diagnostic tool, a proof of concept study in the field of major depressive disorders’, Sci. Rep., vol. 7, 2017.
[10]  P. Philip, S. Bioulac, A. Sauteraud, C. Chaufton, and J. Olive, ‘Could a virtual human be used to explore excessive daytime sleepiness in patients?’, Presence Teleoperators Virtual Environ., vol. 23, no. 4, pp. 369–376, 2014. 


Work environment: 
The PhD student will be hosted at LaBRI in the Image and Sound (I&S) department with frequent visits to SANPSY where he/she will interact with the clinicians and the designers of the virtual agents. 

The I&S department conducts research in acquisition, processing, analysis, modeling, synthesis and interaction of audiovisual media. It works on the entire acquisition chain from data collection to information extraction or restitution of digital data with the user at the center of the chain. The spectrum of manipulated data is very wide: 2D and 3D images, video, speech, music, 3D data, EEG, pysiological data, etc. The different steps of the processing chain integrate modeling phases for analysis or synthesis. The targeted application domains are: health, medical, education, gaming, etc.

The SANPSY unit has a recognized expertise in sleep restriction studies and in the evaluation of countermeasures to sleep deprivation. The team is also specialized in sleep disorders, especially obstructive sleep apnea diagnostic and treatment. The SANPSY unit is located on the neuro-psychopharmacological research platform (PRNPP). This platform is recognized nationally and internationally for its expertise in clinical research, simulation and virtual reality. It has been labeled IBISA in 2015. In 2011, SANPSY obtained an EquipEx project (PHENOVIRT) that aimed to improve phenotyping using simulation and virtual reality technologies. Part of this project, SANPSY has initiated, in particular, the development of Embodied Conversational Agents (virtual doctors and patients). Several scenarios for the diagnosis of drowsiness, depression and addiction to tobacco and alcohol have already been developed and tested in patients. 

Université de Bordeaux CNRS
Jean-Luc ROUAS 
CNRS Researcher
Bordeaux Computer Science Research Laboratory (LaBRI)
351 Cours de la libération - 33405 Talence Cedex - France
T. +33 (0) 5 40 00 35 28
www.labri.fr/~rouas
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6-23(2022-04-15) Doc ou Postdoc au LIG, Grenoble, France
Date limite de candidature : 30 avril 2022

Sujet de thèse ou de postdoctorat dans le cadre du projet  Popcorn (projet collaboratif avec deux entreprises)
encadrée par Benjamin Lecouteux, Gilles Sérasset et Didier Schwab (Laboratoire d’Informatique de Grenoble, Groupe d’Étude en Traduction Automatique/Traitement Automatisé des Langues et de la Parole)

 

Titre : Peuplement OPérationnel de bases de COnnaissances et Réseaux Neuronaux

 

Le projet aborde le problème de l’enrichissement semi-automatisé d’une base de connaissance au travers de l’analyse automatique de textes. Afin d’obtenir une innovation de rupture dans le domaine du Traitement Automatique du Langage Naturel (TALN) pour les clients sécurité et défense, le projet se focalise sur le traitement du français (même si les approches retenues seront par la suite généralisables à d’autres langues). Les travaux aborderont différents aspects :
● L’annotation automatique de documents textuels par la détection de mentions d’entités présentes dans la base de connaissance et leurs désambiguïsation sémantique (polysémie, homonymie) ;
● La découverte de nouvelles entités (personnes, organisations, équipements, événements, lieux), de leurs attributs (âge d’une personne, numéro de référence d’un équipement, etc.), et des relations entre entités (une personne travaille pour une organisation, des personnes impliquées dans un événement, ...). Une attention particulière sera donnée au fait de pouvoir s’adapter souplement à des évolutions de l’ontologie, la prise en compte de la place de l’utilisateur et de l’analyste pour la validation/capitalisation des extractions effectuées.
Le projet se focalise autour des trois axes de recherches suivants :
● Génération de données synthétiques textuelles à partir de textes de référence ;
● La reconnaissance des entités d’intérêt, des attributs associés et des relations entre les entités.
● La désambiguisation sémantique des entités (en cas d’homonymie par exemple)

 

Profil recherché:
    - Solide expérience en programmation & machine learning pour le Traitement Automatique de Langues (TAL), notamment l’apprentissage profond
    - Master/Doctorat Machine Learning ou informatique, une composante TAL ou linguistique computationnelle sera un plus apprécié
    - Bonne connaissance du français

 

Détails pratiques:
    - Début de la thèse rentrée 2022
    - Contrat doctoral à temps plein au LIG (équipe Getalp) pour 3 ans (salaire: min 1768€ brut mensuel)
    - ou Contrat postdoctoral à temps plein au LIG (équipe Getalp) pour 20 mois (salaire: min 2395€ brut mensuel)



 

Environnement scientifique:

 

  • Le doctorat ou le postdoctorat sera mené au sein de l'équipe Getalp du laboratoire LIG  (https://lig-getalp.imag.fr/).
  • La personne recrutée sera accueillie au sein de l’équipe qui offre un cadre de travail stimulant, multinational  et agréable. 
  • Les moyens pour mener à bien le (post)doctorat seront assurés tant en ce qui concerne les missions en France et à l’étranger qu’en ce qui concerne le matériel (ordinateur personnel, accès aux serveurs GPU du LIG, Grille de calcul Jean Zay du CNRS).

 

Comment postuler ?

 

  • Pour postuler sur une thèse de doctorat, les candidats doivent être titulaires d'un Master en informatique, en apprentissage machine ou en traitement automatique du langage naturel (obtenu avant le début du contrat doctoral, les étudiants actuellement en master 2 peuvent ainsi postuler).
  • Pour postuler sur un postdoctorat, les candidats doivent être titulaires d'une thèse de doctorat en informatique,  en apprentissage machine ou en traitement automatique du langage naturel (obtenu avant le début du contrat doctoral, les étudiants dont la soutenance est prévue avant fin septembre 2022 peuvent ainsi postuler).
  • Ils doivent avoir une bonne connaissance des méthodes d’apprentissage automatique et idéalement une expérience en collecte et gestion de corpus.
  • Ils doivent également avoir une bonne connaissance de la langue française. 
Les candidatures doivent contenir : CV + lettre/message de motivation + notes de master + lettre(s) de recommandations; et être adressées à Benjamin Lecouteux (benjamin.lecouteux@univ-grenoble-alpes.fr), Gilles Sérasset (gilles.serasset@univ-grenoble-alpes.fr) et Didier Schwab (Didier.Schwab@univ-grenoble-alpes.fr
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6-24(2022-04-18) PhD studentship at the University of Edinburgh,UK
Hi all,
Here is an offer for a PhD studentship in modelling the articulation of spoken utterances at the University of Edinburgh
The PhD work will be to implement computational models of human speech articulation planning. It will involve the development of software for testing theoretical assumptions, along with tests of software output. The work combines phonetic and phonology aspects, speech technology and motor control theory with programming and software development.
The deadline to apply is 15th May 2022.
 
Information about eligibility and the application process may be found at:
https://www.ed.ac.uk/ppls/linguistics-and-english-language/prospective/postgraduate/funding-research-students/erc-phd-studentship-articulation-spoken-utterances
 
Contacts:
- Alice Turk: a.turk@ed.ac.uk
- Benjamin Elie: benjamin.elie@ed.ac.uk
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6-25(2022-04-15)PhD chez Orange, France

Orange recrute un.e doctorant.e sur le sujet 'Deep learning pour le traitement conjoint du langage naturel et des connaissances'.


L’objectif de la thèse est de proposer des solutions pour mutualiser le traitement de tâches de compréhension et génération du langage naturel. Il s’agira ainsi d’étudier la fusion progressive de diverses tâches mêlant langage naturel et langage(s) formel(s) de représentation ou manipulation de connaissances. Le contexte d’application sera tout d’abord celui d’énoncés isolés, puis celui de dialogues humain-machine où l’historique de discussion doit être pris en compte.

 

Détails et candidature via Orange Jobs : https://orange.jobs/jobs/offer.do?joid=111967&lang=FR

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6-26(2022-04-17) PhD at University of Zurich, Switzerland

Human knowledge is inherently multi-modal, and it is more than just a collection of isolated pieces of information, irrespective of the form of expression. Instead, it emerges from the interconnectedness of all of these information fragments. Knowledge graphs are a powerful way of capturing such interconnected knowledge. Such graphs are effective for storing and relating information that can easily be expressed in textual form, by assigning a simple text label to every node in a graph or relating them to literals represented using strings or blobs. However, they so far fail to capture the richness of information that is not easily expressed as a short piece of text.

The ‘MediaGraph' project aims to extend the concept of a knowledge graph with all types of information which are currently representable as multimedia to be able to capture the richness of human knowledge. In contrast to a knowledge graph whose nodes are associated with a textual label (with a specification arising from relations to other entities and labels), the nodes in a media graph will be able to represent and interrelate any part of any multimedia document. The resulting graph will not only describe the semantic but also the stylistic and technical relations between the documents and their components and form the basis for novel media interaction paradigms.

For this project, we are seeking a motivated PhD student to help make MediaGraph a reality. Requirements include an MSc in Computer Science or a related discipline, a background in both theoretical and applied aspects of computer science as well as a passion for discovering new things. Experience in the areas of databases, data management, semantic web technologies, multimedia processing, multimedia analysis, machine learning, and/or signal processing is considered a plus. The PhD Student will be in charge of the development of manual as well as automated construction methods for MediaGraphs and will define and own some of the practical use-cases to which MediaGraph will be applied in practice. They will also contribute to the design and implementation of representation, querying, and evaluation mechanisms for the graphs.

To apply, please gather your curriculum vitae, all grade transcripts, selected publications (if available), a list of at least three references, and your BSc/MSc theses as PDF files and go to https://www.apply.dsi.uzh.ch/position/5996546.

The University of Zurich is committed to enhancing the number of women in scientific positions and, therefore, particularly invites women to apply. Women who are as qualified for the position in question as male applicants will be given priority.

For more information on the project and the research group, visit https://www.ifi.uzh.ch/en/ddis.html.

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6-27(2022-04-19) Postdoctoral Position at Columbia University in The City of New York, NY, USA

Postdoctoral Position - Machine Learning and Digital Twins, Columbia University in The City of New York.

  • For candidates with a PhD degree in CS, EE, Data Sciences, or a related major.
  • Two years, starting before September 2022.
  • Mentored by Prof. Zoran Kostic, Columbia University Electrical Engineering Department and Data Sciences Institute.
  • Funded by the NSF Cyber Physical Systems Program.
  • Collaboration across multiple departments at Columbia School of Engineering and Data Sciences Institute.
  • For detailed description and instructions on how to express interest, see https://www.aidl.ee.columbia.edu/postdoc .
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6-28(2022-04-20) PhD grant at the University of Edinburg, UK
The University of Edinburghis  looking for a PhD candidate to work on modelling the articulation of spoken utterances as part of Alice Turk's Advanced ERC grant. The 4 year PhD studentship at the University of Edinburgh will be fully funded by the grant. We are looking for candidates with decent programming skills, and an interest in speech analysis and modelling:
 
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6-29(2022-04-23) Doctoral position : Acoustic to Articulatory Inversion by using dynamic MRI images, INRIA, Nancy, France

Doctoral position : Acoustic to Articulatory Inversion by using dynamic MRI images

 

Loria “Lorraine Research Laboratory in Computer Science and its Applications” is a research unit common to CNRS, the Université de Lorraine and INRIA. Loria gathers 450 scientists and its missions mainly deal with fundamental and applied research in computer sciences, especially the MultiSpeech Team which focuses automatic speech processing, audiovisual speech and speech production. IADI is a research unit common to Inserm the Université de Lorraine whose specialty is developing various techniques and methods to improve imaging of moving organs via the acquisition of MR images.

 

This PhD project founded by LUE (Lorraine Université d’Excellence) associates the Multispeech team and the IADI laboratory.

 

Start date is (expected to be) 1st October 2022 or as soon as possible thereafter.

 

Supervisors

Yves Laprie, email yves.laprie@loria.fr

Pierre-André Vuissoz, email pa.vuissoz@chru-nancy.fr

 

The project

 

Articulatory synthesis mimics the speech production process by first generating the shape of the vocal tract from the sequence of phonemes to be pronounced, then the acoustic signal by solving the aeroacoustic equations. Compared to other approaches to speech synthesis which offer a very high level of quality, the main interest is to control the whole production process, beyond the acoustic signal alone.

The objective of this PhD is to succeed in the inverse transformation, called acoustic to articulatory inversion, in order to recover the geometric shape of the vocal tract from the acoustic signal. A simple voice recording will allow the dynamics of the different articulators to be followed during the production of the sentence.

Beyond its interest in terms of scientific challenge, articulatory acoustic inversion has many potential applications. Alone, it can be used as a diagnostic tool to evaluate articulatory gestures in an educational or medical context.

 

Description of work

 

The objective is the inversion of the acoustic signal to recover the temporal evolution of the medio-sagittal slice. Indeed, dynamic MRI provides two-dimensional images in the medio-sagittal plane at 50Hz of very good quality and the speech signal acquired with an optical microphone can be very efficiently deconstructed with the algorithms developed in the MultiSpeech team (examples available on https://artspeech.loria.fr/resources/). We plan to use corpora already acquired or in the process of being acquired. These corpora represent a very large volume of data (several hundreds of thousands of images) and an approach for tracking the contours of articulators in MRI images which gives very good results was developed to process corpora. The automatically tracked contours can therefore be used to train the inversion. The goal is to perform the inversion using the LSTM approach on data from a small number of speakers for which sufficient data exists. This approach will have to be adapted to the nature of the data and to be able to identify the contribution of each articulator. In itself, successful inversion to recover the shape of the vocal tract in the medio-sagittal plane will be a remarkable success since the current results only cover a very small part of the vocal tract (a few points on the front part of the vocal tract). However, it is important to be able to transpose this result to any subject, which raises the question of speaker adaptation, which is the second objective of the PhD.

 

What we offer

  • A position funded by LUE (Lorraine Université d’Excellence) at a leading technical university that generates knowledge and skills for a sustainable futur.
  • A very complementary scientific environment of the two teams (MultiSpeech and IADI) in all fields of MRI and anatomy in the IADI laboratory and in deep learning and speec processing in the MultiSpeech team of Lori.

  • Engaged and ambitious colleagues along with a creative, international and dynamic working environmen.

  • At Loria, there are lively research groups in a number of areas, for example natural language processing, deep learning, computer graphics, robotics… At the moment, there are about 150 PhD students at Loria and IADI.

  • Works in the very center of Europe in close proximity to nature.

  • Help to relocate and be settled in France and at Université de Lorraine.

 

Supervisors

Yves Laprie, email yves.laprie@loria.fr

Pierre-André Vuissoz, email pa.vuissoz@chru-nancy.fr

 

Application

Your application including all attachments must be in English and submitted electronically by clicking APPLY NOW below.

Please include

  1. Motivated letter of application (max. one page)

  2. Your motivation for applying for the specific PhD project

  3. Curriculum vitae including information about your education, experience, language skills and other skills relevant for the position

  4. Publication list (if possible)

  5. Reference letters (if available)


The deadline for applications is Friday 13 May 2022, 23:59 GMT +2
.

log into Inria’s recruitment system (https://jobs.inria.fr/public/classic/en/offres/2022-04654in order to apply to this position.

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6-30(2022-04-26) Position of University Assistants (prae doc), University of Vienna, Austria


The University of Vienna seeks to fill the position starting Oct 1, 2022
of University Assistants (prae doc)
at the Research Group MIS - Multimedia Information Systems
at the Faculty of Computer Science, University of Vienna
under the supervision of Prof. Klas
Reference number: “#01 PC3: Multimedia Information Systems, Prof. Klas'

Please see the job announcement at
https://docs.univie.ac.at/how-to-apply/pooled-calls/

Please note that good German language skills are required for this position due to the role in teaching.
==
The Faculty of Computer Science of the University of Vienna has world-leading researchers in Computer Science who pursue basic as well as applied research. The UniVie Doctoral School Computer Science (DoCS) builds an essential framework to foster excellence in research and teaching. Its main focus is young prospective researchers eager to make an impact on both basic research as well as applied problems with collaborations across the University and beyond. The DoCS aims to provide these young researchers with the broad knowledge and expertise needed to perform Computer Science research at the highest achievable quality. The Doctoral School trains doctoral candidates in solving basic as well as applied research questions of high relevance. The Research Group Multimedia Information Systems at the Faculty of Computer Science is looking for highly motivated research assistants, Ph.D. candidates. We are offering an excellent working environment in a young, creative, highly motivated, and international team, manifold opportunities for personal development, the possibility to take responsibility in research projects at an early stage, the opportunity for research stays abroad, excellent industrial contacts, and intensive support in the course of a dissertation.

Duration of employment: 4 years (The announcement is made for four years, whereby the employment relationship is initially limited to 1.5 years and is automatically extended to a total of four years unless the employer submits a declaration of non-renewal after a maximum of 12 months.)

Extent of employment: 30.0 hours/week
Job grading in accordance with collective bargaining agreement: §48 VwGr. B1 Grundstufe
(praedoc) with relevant work experience determining the assignment to a particular salary
grade.

Job description
Participation in research, teaching, and administration:
- Participation in research projects/research studies
- Participation in publications/academic articles/presentations
- We expect the successful candidate to sign a doctoral thesis agreement within 12-18 months.
- Participation in teaching and independent teaching of courses as defined by the collective agreement
- Supervision of students
- Involvement in the organisation of meetings, conferences, symposiums
- Involvement in the department administration as well as in teaching and research administration
The research should focus on techniques for the automated detection of misinformation in multimedia content.

Profile
- Master in Computer Science, or Media Technologies, or Business Informatics (or equivalent qualification, with a strong focus on Computer Science) with excellent marks.
- Excellent command of written and spoken German and English.
- High ability to express yourself both orally and in writing
- Strong motivation to work in a team environment.
- Strong motivation to publish at top-refereed conferences/journals.
- Perseverance, focus, integrity, ability to make things done, leadership in organization and partnership/communication with other project teams.
- Comprehensive IT user skills

Desirable qualifications are
- Excellent knowledge in the fields of multimedia systems, digital media technologies, semantic analysis of multimedia content, techniques for the detection of misinformation, user interface, and feedback systems, various programming languages (Java, C, C++, C#, Python, JavaScript), technologies in the area of Internet-of-Things, web-based systems, databases, blockchain systems, cloud computing, formal methods in CS
- Expertise in various application areas of Computer Science
- Proven interest in scientific work and in scientific publishing
- Basic experience in research methods and academic writing
- Knowledge of university processes and structures - Experience in remote teaching using modern IT infrastructures

Application documents
- Curriculum vitae
- Letter of Motivation including ideas for a prospective doctoral project proposal
- Abstract of master thesis
- Degree certificates
- List of publications, evidence of teaching experience (if available)

Applications need to be submitted via the recruiting tool Apply@DoCS | Servicedesk Universität Wien (univie.ac.at), mentioning reference number “#01 PC3: Multimedia Information Systems, Prof. Klas”.
Further details are available here (also how you set up an u:account): https://docs.univie.ac.at/apply.

The University pursues a non-discriminatory employment policy and values equal opportunities, as well as diversity (http://diversity.univie.ac.at/). The University lays special emphasis on increasing the number of women in senior and academic positions. Given equal qualifications, preference will be given to female applicants.

The candidates who are selected for the position join the DoCS as doctoral student members.

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6-31(2022-04-30) 3 PhD fellowships at the University of Copenhaguen, Denmark

3 PhD fellowships in applied Machine Learning, Information Retrieval and Natural Language Processing

The Information Retrieval Lab of the Department of Computer Science at the University of Copenhagen (DIKU) is offering 3 fully funded PhD Fellowships in applied Machine Learning, Information Retrieval, and Natural Language Processing, commencing 1 September 2022 or as soon as possible thereafter.

The fellows will conduct research, having as starting point the following broad research areas:
  • a fully-funded PhD in interpretability of applied machine learning;
  • a fully-funded PhD in overparameterization and generalizability in deep neural architectures;
  • a fully-funded PhD in web & information retrieval;
 
We are looking for candidates with a MSc degree in a subject relevant for the research area. The successful candidate is expected to have strong grades in Machine Learning and/or Information Retrieval and/or Natural Language Processing. Successful candidates should have a preliminary research record as witnessed by a master thesis or publications in the area.

The deadline for applications is 19 May 2022, 23:59 GMT +2.
 
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6-32(2022-05-06) PhD students, Postdoctoral Researchers and R&D Engineers at Telecom Paris, Palaiseau, France

We have multiple openings for PhD studentsPostdoctoral Researchers and R&D Engineers at Télécom Paris, Institut polytechnique de Paris, in the “Signal, Statistics and Learning (S2A) team. 

 

All positions are located at Telecom Paris, 19 place Marguerite Perey, 91120 Palaiseau, France.

 

Start of the positions: October/November 2022 (for PhDs/Engineer), January 2023 for PostDoc

 

Subject:

The positions will be a part of the ERC Advanced (2022) – HI-Audio (Hybrid and Interpretable Deep neural audio machines) project, which aims at building hybrid deep approaches combining parameter-efficient and interpretable models with modern resource-efficient deep neural architectures with applications in speech/audio scene analysis, music information retrieval and sound transformation and synthesis.

 

The potential topics include (and are not limited to):

- Deep generative models, adversarial learning

- Attention-based models and curriculum learning

- Statistical/deterministic audio models (signal models, sound propagation models,…)

- Music Information Retrieval software platform development (R&D Engineer position)

 

 

Candidate Profile: 

- For the Phd positions: A masters degree in applied mathematics, datascience/computer science or speech/audio/music processing is required.

- For the Postdoc position: PhD degree and publications in theory or applications of machine learning, generative modelling, discrete optimal transport or signal processing, ideally with applications to Speech/Audio/Music signals.

- Master internship positions will also be open in early 2023.

 

Télécom Paris, and the S2A  team:

 

The S2A team gathers 18 permanent faculties covering a wide variety of research topics including Statistics, Probabilistic modeling, Machine learning, Data science, Audio and social signal processing. On the overall, Télécom Paris’ research counts 19 research teams and covers various domains in computer science and networks, applied mathematics, electronics, image, data, signals and economic and social sciences. Télécom Paris (https://www.telecom-paris.fr/en/home) is a member of IMT (Institut Mines-Télécom), and is a founding member of the Institut Polytechnique de Paris (IP Paris, https://www.ip-paris.fr/en), a world-class scientific and technological institution which is a partnership between five prestigious French engineering schools  with HEC as a key partner.

 

Application:
- There is no specific deadline. Applications are welcome until all positions are filled.

- In the application, please send a resume, a motivation letter (and full transcript grades for Phd/Engineer positions) to Gaël Richard, firstname.lastname@telecom-paris.fr. At least one reference letter will be asked in a second step.

 

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