We have multiple openings for PhD students, Postdoctoral 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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Multichannel Speech Enhancement for Patients with Auditory Neuropathy Spectrum Disorders

Location: LORIA, MULTISPEECH team, Nancy

Supervisors: Romain Serizel (Maître de Conférences, Université de Lorraine), Paul Magron (Chargé de Recherche, INRIA).

This PhD fits within the scope of the ANR project 'REFINED' involving the Multispeech research team in (LORIA), Nancy, the Laboratory of Embedded Artificial Intelligence in CEA (List) in Paris, and the Hearing Institute in Paris. Context Worldwide, around 466 million people currently suffer from a hearing loss. To remedy the loss of hearing sensitivity, portable hearing aids have been designed for almost a century. Regardless of the recent advances in audio signal processing integrated in current hearing aids models, people suffering from Auditory Neuropathy Spectrum Disorders enjoy little or no benefit from current hearing aids [1]. Contrary to regular hearing losses, Auditory Neuropathy Spectrum Disorders impair the processing of temporal information without necessarily affecting auditory sensitivity. This can have a particularly dramatic impact in scenarios where the speech of interest is present together with some background noise or with one or several concurrent speaker(s). Current speech enhancement systems are usually trained on generic corpora, and they are designed to optimize some cost between the target (known) speech and the output of the system, which is estimated from the mixture, such as the mean squared error [2] or the speech-to-distortion ratio [3]. The trained system is then evaluated using a criterion that is designed to reflect the speech perception from people without hearing losses [4]. Yet, the main need of subjects with Auditory Neuropathy Spectrum Disorders, shared with ageing subjects who experience central auditoryprocessing difficulties, is not to restore audibility but to improve their speech intelligibility, particularly in noisy environments, by compensating for the deterioration of acoustic cues that rely on temporal precision [5]. Objectives Based on clinical studies performed at the Hearing Institute within the project, the main goal of this PhD is to define new cost functions to be optimized by the speech processing algorithms that are more relevant for subjects with Auditory Neuropathy Spectrum Disorders than generic losses used in current algorithms. We will pay particular attention to the algorithms’ ability to help volunteers in scenarios with multiple potential target sources that are spatially distributed in a room. We will derive the speech enhancement filters aiming to extract not only speech, but also additional cues such as speech contour or timbre. In a latter step, the model will be adapted under light human supervision in order to reduce the burden of the usual iterative “handcrafted” adjustments and repeated visits with a specialist clinician to fit the hearing aid to individual needs.

Profile • Strong background in audio signal processing or machine learning • Excellent programming skills • Excellent English writing and speaking skills Application Upload your application on ADUM (https://www.adum.fr/as/ed/voirproposition.pl? site=adumR&matricule_prop=43498#version) with the following: • CV • Cover letter • Recommendation letter • M1-M2 note transcript • Master thesis, if available

References [1] Berlin, C. I. et al. Multi-site diagnosis and management of 260 patients with auditory neuropathy/dys-synchrony (auditory neuropathy spectrum disorder). Int J Audiol 49, 30-43 (2010). [2] Doclo, S., Spriet, A., Wouters, J. & Moonen, M. Frequency-domain criterion for the speech distortion weighted multichannel Wiener filter for robust noise reduction. Speech Communication 49, 636-656 (2007). [3] Luo, Y., et al. FaSNet: Low-latency adaptive beamforming for multi-microphone audio processing. 2019 IEEE automatic speech recognition and understanding workshop (2019). [4] Vincent, E., Rémi G., and Cédric F. Performance measurement in blind audio source separation. IEEE transactions on audio, speech, and language processing 14.4, 1462-1469 (2006). [5] https://claritychallenge.github.io/clarity_CC_doc/docs/cpc1/cpc1_intro

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Cross-lingual and cross-domain terminology alignment

Interested in joining a young NLP group of 10+ people located in a historical town by the Atlantic Ocean? And walk 10 minutes from the lab to the beach. We have open positions in the context of recent Horizon 2020 projects: Embeddia and NewsEye as well as related local projects. In the last 2 years, we have among others published long papers in CORE A* and A conferences such as ACL, JCDL, CoNLL, ICDAR, COLING, ICADL, etc.  

Location: L3i laboratory, La Rochelle, France

Duration: 2 years (1+1), with possible further extension

Net salary range: 2100€-2300 € monthly

Context: H2020 Embeddia project and regional project Termitrad

Start: September 2022 (tentatively)

Keywords: terminology alignment, cross-lingual word embeddings, named-entity recognition and linking, deep/machine learning, statistical NLP, (text) mining.

Applications are invited for a postdoctoral researcher position around the topic of project Termitrad: keyword and terminology alignment 1) across languages and 2) across domains. In short, the overall objective of the project is to improve the relevance of the keywords describing research papers (and, time allowing, the quality of abstracts). One the one hand (cross-lingual alignment), we will rely on a corpora of journal articles with both French and English keywords and abstracts, both in as written by authors and in versions curated by experts. On the other hand (crossdomain alignment), we will work with use cases provided by researchers from different fields using different terms to describe similar concepts.

To address this very project, the project team will consist of senior staff, 2 post-doctoral researchers and 2-3 PhD students, one of which is jointly supervised in the Józef Stefan Institute in Ljubljana, coordinator of H2020 Embeddia. In this context, you will first be in charge of building a state of the art of existing related approaches, tools and resources, then to conduct further research and experiments, as well as participate in the supervision of PhD students.

Who we search for:

-    - PhD in statistical NLP, IR, or ML, ideally with further postdoctoral experience

-    - proven record of high-level publications in one or more of those fields

-    - fluency in written and spoken English (French language skills are welcome but unnecessary)

Applications including a CV and a one-page research statement discussing how the candidate's background fits requirements and topic are to be sent to by email to  antoine.doucet@univ-lr.fr, strictly with the subject 'Embeddia/Termitrad postdoc application'.

Application deadline: 14 June 2022.

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-- Post-doctoral research position - L3i - La Rochelle France
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Title : Emotion detection by semantic analysis of the text in comics speech balloons

The L3i laboratory has one open post-doc position in computer science, in the specific field of natural language processing in the context of digitised documents.

Duration: 12 months (an extension of 12 months will be possible)

Position available from: As soon as possible

Salary: approximately 2150 € / month (net)

Place: L3i lab, University of La Rochelle, France

Specialty: Computer Science/ Document Analysis/ Natural Language Processing

Contact: Jean-Christophe BURIE (jcburie [at] univ-lr.fr) / Antoine Doucet (antoine.doucet [at] univ-lr.fr)

Position Description

The L3i is a research lab of the University of La Rochelle. La Rochelle is a city in the south west of France on the Atlantic coast and is one of the most attractive and dynamic cities in France. The L3i works since several years on document analysis and has developed a well-known expertise in ‘Bande dessinée”, manga and comics analysis, indexing and understanding.

The work done by the post-doc will take part in the context of SAiL (Sequential Art Image Laboratory) a joint laboratory involving L3i and a private company. The objective is to create innovative tools to index and interact with digitised comics. The work will be done in a team of 10 researchers and engineers.

The team has developed different methods to extract and recognise the text of the speech balloons. The specific task of the recruited researcher will be to use Natural Language Processing strategies to analyse the text in order to identify emotions expressed by a character (reacting to the utterance of another speaking character) or caused by it (talking to another character). The datasets will be collections of comics in French and English.

Qualifications

Candidates must have a completed PhD and a research experience in natural language processing. Some knowledge and experience in deep learning is also recommended.

General Qualifications

• Good programming skills mastering at least one programming language like Python, Java, C/C++

• Good teamwork skills

• Good writing skills and proficiency in written and spoken English or French

Applications

Candidates should send a CV and a motivation letter to jcburie [at] univ-lr.fr and antoine.doucet [at] univ-lr.fr.

Applications will be considered from 9 June onwards, and until a candidate is hired

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Postdoctoral fellowship in corpus phonetics / data science for speech, Northwestern University
Applications are invited for a postdoctoral fellowship (contingent on final approval of funding) for a National Science Foundation grant entitled, Enhancing speech and deep learning research through holistic acoustic analysis. This project will test a novel, unsupervised deep learning approach that discovers a representational space for analysis of acoustic variation. To test this highly general approach, we will assess whether it out-performs a baseline, based on current methods for analyzing individual variation in bilingual speech.
 
The postdoctoral fellow will be co-supervised by Matt Goldrick (PI) and Ann Bradlow (co-I). The fellow will help develop automated workflows for a number of grant components, including: the baseline technique for measuring acoustic variation using current (off-the-shelf) automatic acoustic analysis methods; online intelligibility testing over large numbers (1000+) participants; and online free-classification techniques.
 
The fellow will work in collaboration with research teams at other grant sites: Technion University (lead by Joseph Keshet) and the University of California at San Diego (lead by Tamar H. Gollan). The project will support travel by the fellow to one of these other sites and other opportunities for professional development.
 
Desired qualifications include experience with corpus phonetics techniques including data-frame/corpus design, phonetic processing and scripting in Praat, online speech recognition testing with automatic scoring, and data visualization and analysis with R. 
 
The position is for one year, with the potential for renewal for a second year. The pay rate will follow NIH postdoctoral fellows stipend levels. The position can start as early as September 1, 2022, and ideally would begin before January 1, 2023. Applications should be submitted to matt-goldrick@northwestern.edu. Applications should include a CV (including contact information and links to written work) and the names of two references (letters will be requested after initial review of applications). Application review will begin June 20, 2022.  Email inquiries about the position should be directed to Matt Goldrick (matt-goldrick@northwestern.edu) or Ann Bradlow (abradlow@northwestern.edu).

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Inria is opening a fully funded PhD position on multimodal speech
anonymization. For details and to apply, see:
https://jobs.inria.fr/public/classic/en/offres/2022-05013

Applications will be reviewed on a continuous basis until June 30.

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Ingénieur.e science des données et corpus – Laboratoire d’Informatique de Grenoble

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Job opportunity: Associate Teaching Professor at the University of Cambridge, Department of Engineering

We're advertising for an Associate Teaching Professor who will be the Course Director of the Machine Learning and Machine Intelligence (MLMI) MPhil. The post will involve teaching and the post-holder can be research active e.g. they can start and run their own research group. The main expertise could be in any field related to the MPhil including: machine learning, machine intelligence, speech and language processing, signal processing, control, robotics, human-computer interaction, computer vision, and high performance computing.

Advert: https://www.jobs.cam.ac.uk/job/35215/

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Job opportunity: Associate Teaching Professor at the University of Cambridge, Department of Engineering

We're advertising for an Associate Teaching Professor who will be the Course Director of the Machine Learning and Machine Intelligence (MLMI) MPhil. The post will involve teaching and the post-holder can be research active e.g. they can start and run their own research group. The main expertise could be in any field related to the MPhil including: machine learning, machine intelligence, speech and language processing, signal processing, control, robotics, human-computer interaction, computer vision, and high performance computing.

Advert: https://www.jobs.cam.ac.uk/job/35215/

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Le CNRS ouvre un poste attractif en Apprentissage Automatique pour le Traitement Automatique des Langues. Il s'agit d'une 'Chaire Professeur Junior', un type de poste créé cette année, qui offre un accès direct à un poste permanent de Directrice ou Directeur de Recherche CNRS au bout de 3 à 6 ans.

L'objectif affiché de cette campagne est de renforcer un des trois laboratoires d'excellence suivants en TAL : LISN, LIG, LORIA, sur des aspects stratégiques ou souverains : le traitement automatique de la langue française écrite ou orale, des domaines d'application spécifiques ayant peu de données d'apprentissage ou faiblement couverts par les modèles génériques (par exemple, dans les domaines de la sécurité, la défense ou la santé), ou encore en lien avec des applications et plateformes éducatives ou d'assistance aux personnes handicapées. Ce poste inclut une charge d'enseignement de seulement 42 heures par an pendant toute la durée du contrat. Ensuite, la fonction de directrice ou directeur de recherche CNRS n'impose aucune charge d'enseignement obligatoire.

La prise de fonction sur ce poste est accompagnée d'un environnement de 300,000 € (200,000 € de l'ANR plus une bourse de thèse).

La date limite de dépôt des dossiers est le : 31/08/2022
La date prévue d'embauche est le : 01/12/2022

Tous les détails concernant cette offre de poste du CNRS sont donnés ici :

https://www.cnrs.fr/en/cnrsinfo/join-cnrs-25-tenure-track-positions-available

L'offre spécifique en Apprentissage Automatique pour le Traitement Automatique des Langues est détaillée ici :

https://emploi.cnrs.fr/Offres/CPJ/CPJ-2022-020/Default.aspx?Lang=EN


Contacts :

LISN: Gilles Adda (Gilles.Adda@lisn.upsaclay.fr)

LIG: François Portet (francois.portet@imag.fr)

LORIA: Christophe Cerisara (christophe.cerisara@loria.fr)

Les activités d'enseignement prendront part dans les programmes dédiés mis en place par l'Université de Paris Saclay (contact : Dominique Quadri presidence-dept-info.sciences@u-psud.fr), l'Université de Grenoble Alpes (contact : Massih-Reza.Amini@grenoble-inp.fr), ou l'Université de Lorraine (contact : Maxime Amblard maxime.amblard@loria.fr) dans le domaine de l'intelligence artificielle.

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To the Speech Prosody Special Interest Group,

We are looking for two postdocs (2 years each in the first instance) to work on intonation and intonation pragmatics as part of SPRINT (sprintproject.io). We would appreciate it if you could share the information with your networks, especially because we have a short deadline for applications (19 June).

The job descriptions and other details can be found at the links below, but potential applicants can get in touch with me as well (using my Radboud address, amalia.arvaniti@ru.nl)

Please note that the previous links we sent don’t work, these are the new links and should work.

https://www.ru.nl/english/working-at/vacature/details-vacature/?ruid=1583&pad=%2fenglish&doel=embed&taal=uk

https://www.ru.nl/english/working-at/vacature/details-vacature/?ruid=1585&pad=%2fenglish&doel=embed&taal=uk

regards to all,

Amalia Arvaniti

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National Yang Ming Chiao Tung University (NYCU), one of the top-ranked universities in Taiwan, invites applications for multiple faculty positions (assistant, associate, full, and chair professors), in the Institute of Artificial Intelligence Innovation (IAII). IAII is part of the newly established Industry Academia Innovation School (IAIS) in NYCU, with major focuses on innovations in artificial intelligence (https://iais.nycu.edu.tw/). IAII/NYCU is located in Hsinchu Science Park, Taiwan’s “Silicon Valley”, wherein over two thirds of the CEOs and managers are NYCU graduates.

With the determination to facilitate industry–government–academia–research collaboration to drive the next-generation industry development, we are looking for strong candidates in the broader area of artificial intelligence, data science, security, information engineering, broadband communication, and Internet of Things. Applicants are expected to conduct outstanding research and be committed to teaching, in collaboration with world-class ICT industry partners.

Applicants should submit the following items: ● Cover letter ● Curriculum Vitae ● Research statement ● Teaching statement ● Publication list ●      Three or more reference letters ● Any other PDF-formatted supporting materials (optional). Please address all inquiries and nominations to Prof. Wen-Huang Cheng, Director of the IAII via email (whcheng@nycu.edu.tw).

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The Johann Wolfgang Goethe University Frankfurt am Main is one of the largest universities in Germany with around 48,000 students and with about 5,000 employees. Founded in 1914 by Frankfurt citizens and since 2008 once again proud of its foundation status Goethe University possesses a high degree of autonomy, modernity and professional diversity. As a comprehensive university, the Goethe University offers a total of 16 departments on five campuses and more than 100 degree programs along with an outstanding research reputation.

The Institute of Linguistics at the Department of Modern Languages of Goethe University Frankfurt am Main offers a position in cotutelle with the Department of Translation and Language Sciences at Universitat Pompeu Fabra, Barcelona, in the project “Co-Speech Gestures and Prosody as Multimodal Markers of Information Structure” as a

Doctoral Researcher

(E13 TV-GU, 65% part-time) starting October 1st 2022

funded for 3 years by the German Science Foundation (Deutsche Forschungsgemeinschaft DFG). The salary grade includes social benefits and is based on the job characteristics of the collective agreement applicable to Goethe University (TV-G-U).

We offer a 3-year doctoral co-tutelle position to work within a collaborative team. The main focus of the doctoral research will be to assess the multimodal markers of IS in Catalan. The project will be run in close collaboration with the team assessing the multimodal markers of IS in German. The two teams involved are the Prosodic Studies Group at UPF (IP: Dr. Pilar Prieto)

and the Phonology Lab at the Institute of Linguistics in Frankfurt (PI: Dr. Frank Kügler). The project is part of the DFG Priority Progamme 2329 “Visual Communication” (https://vicom.info).

The ideal candidate has a strong background in linguistics and linguistic experimentation, is highly motivated and interested in the assessment of prosodic and gestural markers in language. Knowledge of Catalan will be required to run the experiments. To qualify for a doctoral position in Linguistics the candidate should hold a master’s degree in Linguistics, Philology, Psychology, or equivalent.

Doctoral students are expected to participate actively in the project and the two department’s activities. The position comes with no teaching obligation.

The application in English should consist of one pdf file containing:

• A letter of intent describing your research interests and motivation for PhD studies (maximum one page)
• CV
• English certificate of your BA and MA degree (or equivalent) with a transcript of records
• A maximum 3-page research proposal which states a research question related to the project, describes the methodology and work plan, and contextualises the expected results in relation to the state of the art. The actual dissertation project of the successful candidate will be worked out in collaboration with the supervisor.
• One or two letters of reference
• Other documents which the applicant would like to include

All required documents should be emailed as a pdf file (preferably as one document) to Frank Kügler (Kuegler@em.uni-frankfurt.de) and Pilar Prieto (pilar.prieto@upf.edu) up to July 15th, 2022.

For more information, please contact Frank Kügler and Pilar Prieto.

The Goethe University is committed to a policy of providing equal employment opportunities for both men and women alike, and therefore encourages particularly women to apply for the position/s offered. Individuals with severe disability will be prioritized in case of equal qualification.

--

Prof. Dr. Frank Kügler
Institut für Linguistik
Goethe-Universität Frankfurt
Norbert-Wollheim-Platz 1
D-60323 Frankfurt am Main
Telefon +49 69 798 32217
E-Mail: kuegler@em.uni-frankfurt.de

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We are looking for two postdocs (2 years each in the first instance) to work on intonation and intonation pragmatics as part of SPRINT (sprintproject.io). We would appreciate it if you could share the information with your networks, especially because we have a short deadline for applications (19 June).

The job descriptions and other details can be found at the links below, but potential applicants can get in touch with me as well (using my Radboud address, amalia.arvaniti@ru.nl)

Please note that the previous links we sent don’t work, these are the new links and should work.

https://www.ru.nl/english/working-at/vacature/details-vacature/?ruid=1583&pad=%2fenglish&doel=embed&taal=uk

https://www.ru.nl/english/working-at/vacature/details-vacature/?ruid=1585&pad=%2fenglish&doel=embed&taal=uk

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Post-Doctoral/PhD position at Telecom-Paris on Deep learning approaches for social computing

*Place of work* Telecom Paris, Palaiseau (Paris outskirt)

*Starting date* From September 2022 (but can start later)

*Context*

The PhD student/post-doctoral fellow will take part in the REVITALISE project, funded by ANR ( viRtual bEhaVioral skIlls TrAining for pubLIc SpEaking). The research activity will bring together the research topics of Prof. Chloé Clavel [Clavel] of the S2a [SSA] team at Telecom-Paris– social computing [SocComp] - and Dr. Mathieu Chollet [Chollet] from University of Glasgow – multimodal systems for social skills training, and Dr Beatrice Biancardi [Biancardi] – Social Behaviour Modelling from CESI Engineering School, Nanterre.

* Candidate profile*

As a minimum requirement, the successful candidate should have:

• A master degree 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 cover letter

• The contact of two referees

For the post-doctoral fellow position, additional documents are required:

• The defense and Phd reports

• The contact of two referees

The pdf file should be sent to the three supervisors: Chloé Clavel, Beatrice Biancardi and Mathieu Chollet: chloe.clavel@telecom-paris.fr, bbiancardi@cesi.fr, mathieu.chollet@glasgow.ac.uk

Multimodal attention models for assessing and providing feedback on users’ public speaking ability

*Keywords* human-machine interaction, attention models, recurrent neural networks, Social Computing, natural language processing, speech processing, non-verbal behavior processing, multimodality, soft skills, public speaking

*Supervision* Chloé Clavel, Mathieu Chollet, Beatrice Biancardi

*Description* Oral communication skills are essential in many situations and have been identified as core skills of the 21st century. Technological innovations have enabled social skills training applications which hold great training potential: speakers’ behaviors can be automatically measured, and machine learning models can be trained to predict public speaking performance from these measurements and subsequently generate personalized feedback to the trainees.

The REVITALISE project proposes to study explainable machine learning models for the automatic assessment of public speaking and for automatic feedback production to public speaking trainees. In particular, the recruited intern will address the following points:

-   identify relevant datasets for training public speaking and prepare them for model training

-   propose and implement multimodal machine learning models for public speaking assessment and compare them to existing approaches in terms of predictive performance.

-   integrate the public assessment models to produce feedback a public speaking training interface, and evaluate the usefulness and acceptability of the produced feedback in a user study

The results of the project will help to advance the state of the art in social signal processing, and will further our understanding of the performance/explainability trade-off of these models.

The compared models will include traditional machine learning models proposed in previous work [Wortwein] and sequential neural approaches (recurrent networks) that integrate attention models as a continuation of the work done in [Hemamou_a], [Hemamou_b][BenYoussef]. The feedback production interface will extend a system developed in previous work [Chollet21].

Selected references of the team:

[Hemamou_a] 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

[Hemamou_b] Leo Hemamou;Arthur Guillon;Jean-Claude Martin;Chloe Clavel, Multimodal Hierarchical Attention Neural Network: Looking for Candidates Behaviour which Impact Recruiter’s Decision, IEEE Trans. of Affective Computing, Sept. 2021

[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.

[Wortwein] Torsten Wörtwein, Mathieu Chollet, Boris Schauerte, Louis-Philippe Morency, Rainer Stiefelhagen, and Stefan Scherer. 2015. Multimodal Public Speaking Performance Assessment. In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction (ICMI '15). Association for Computing Machinery, New York, NY, USA, 43–50.

[Chollet21] Chollet, M., Marsella, S., & Scherer, S. (2021). Training public speaking with virtual social interactions: effectiveness of real-time feedback and delayed feedback. Journal on Multimodal User Interfaces, 1-13.

Other references:

[TPT] https://www.telecom-paristech.fr/eng/ 

[IMTA] https://www.imt-atlantique.fr/fr

[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/#

[PACCE] https://www.ls2n.fr/equipe/pacce/

[Clavel] https://clavel.wp.imt.fr/publications/

[Chollet] https://matchollet.github.io/

[Biancardi] https://sites.google.com/view/beatricebiancardi

-Rasipuram, Sowmya, and Dinesh Babu Jayagopi. 'Automatic multimodal assessment of soft skills in social interactions: a review.' Multimedia Tools and Applications (2020): 1-24.

-Sharma, Rahul, Tanaya Guha, and Gaurav Sharma. 'Multichannel attention network for analyzing visual behavior in public speaking.' 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018.

-Acharyya, R., Das, S., Chattoraj, A., & Tanveer, M. I. (2020, April). FairyTED: A Fair Rating Predictor for TED Talk Data. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 01, pp. 338-345).

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Candidat(e) pour une thèse de doctorat en informatique sur un projet collaboratif susceptible d'être financé par la DGA. La thèse se déroulera au sein de l'équipe R2I ( Recherche d'informations et interactions) du pôle Sciences des données du LIS (Marseille)

Sujet de thèse de doctorat

Titre : Génération automatique de résumés fluides de textes en français par apprentissage profond

Encadrement : Prof. Patrice BELLOT (https://cv.archives-ouvertes.fr/patrice-bellot ; Université d’Aix-Marseille CNRS, LIS), Adrian CHIFU (https://adrianchifu.com ; Université d’Aix-Marseille CNRS, LIS) 

Période : octobre 2022 - septembre 2025

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Two PhD positions at Quality and Usability Lab, Technical University of Berlin, Germany

Two PhD positions at Quality and Usability Lab, Technical University of Berlin, Germany

We are looking to recruit two Doctoral Researchers to join Quality and Usability Lab, at Technical University of Berlin, Germany. Both positions are research assistant positions (TVL-E13) and depending on follow up funding to be continued until doctorate thesis can be finished.

The Quality and Usability Lab is part of TU Berlin’s Faculty IV and deals with the design and evaluation of human-machine interaction, in which aspects of human perception, technical systems and the design of interaction are the subject of our research. We focus on self-determined work in an interdisciplinary and international team; for this we offer open and flexible working conditions that promote scientific and personal exchange and are a prerequisite for excellent results.

1. Research Assistant (full-time) Speech Quality

The research is in the area of the assessment of the quality of speech services using a crowdsourcing approach. The aim of the research is to analyze how crowdsourcing-based listening-only and conversational speech quality evaluation experiments can be set up in order to provide valid and reliable results, and how the characteristics of the test participants, the test environment and the playback system can be assessed in online tests. It will be assessed which differences are to be expected between crowdsourcing and laboratory-based speech quality evaluation, and how these differences influence the development of instrumental speech quality prediction models. The results are expected to influence methods for speech quality assessment in crowdsourcing, as they are summarized in ITU-T Recommendation P.808.

This project is funded by the Deutsche Forschungsgemeinschaft, DFG, and is limited to a duration until January 31, 2024 (compensation TVL E13). A subsequent ongoing employment is supported if the PhD cannot be finished in running time of the project

1. Tasks:

• Interacting and extending web platforms that are created for conducting and managing listening-only or conversational experiments (Frontend: HTML/JS/CSS, backend for conversation testing: Node.js / express.js, WebRTC)

• Conducting subjective listening-only and/or conversation tests in the laboratory and via crowdsourcing; Analyzing the results

• Recording of source speech signals in both laboratory and large-scale crowdsourcing and preparing speech dataset.

• Enhancing test methods (that we developed for ITU-T Rec. P.808) for screening the participants’ ability, environment and set-up suitability for the speech quality assessment tasks.

• Processing speech signals collected in a crowdsourcing approach, and applying relevant artificial network degradation conditions (e.g. background noise, clipping, etc.).

• Benchmarking state-of-the-art instrumental models for predicting speech quality based on their performance on the collected crowdsourcing dataset.

• Project communication and reporting.

• Publication and presentation of project and research results in scientific journals, at conferences, at workshops and ITU-T Study Group 12 expert’s meetings. Publication and presentation of project and research results in scientific journals, at conferences, and in workshops as well as standardization meetings of ITU-T

1. Requirements:

• Successfully completed university degree (Master, Diplom or equivalent) in computer engineering/science, informatics, media informatics, digital media, or information systems (or an equivalent technical background)

• Deep knowledge, and hands-on experience in one or more general purpose programming languages (recommended is Python)

• Profound programming skills in front-end (HTML5/CSS3, JS, jQuery, JSON), AND one scripting language for data processing (either MATLAB, Python or R), and ideally backend development skills

• Knowledge about digital signal processing, beneficial: speech signal processing respectively audio signal processing and acoustics

• Knowledge about empirical subjective tests and statistical data analysis is appreciated

• Language skills: English fluent in writing and speaking (B2 level); willingness to learn German is expected

• Joy of working in an interdisciplinary and international environment

2. Research Assistant (full-time) Conversation Quality – Salary grade E 13 TV-L

The position is open to do research in the field of speech signals analysis, and the assessment of speech quality in different (mobile and fixed) networks. Therefore, speech signals are to be analyzed in listening-only as well as conversational situations in order to get indications or the perceived quality. Based on these analysis, signal-based and parametric models for the estimation of speech quality can be extended and integrated. One focus of the present research may be the evaluation of new speech codecs in different network scenarios. The models are to be validated based on subjective listening and conversation tests.

The initial funding is available from September 1^st, 2022 and is limited until April 30^th, 2023; however, the outcomes of the research should be used to support the preparation of a new project application, and may also become a foundation for a later PhD thesis. A subsequent position as a research assistant from the project funds would be possible if the funds were approved.

2.1 Tasks

• Maintaining and further developing a platform to conduct web-based voice calls

• Conducting subjective conversation tests in the laboratory and via crowdsourcing

• Analysis of speech signals

• Creating and evaluating models for predicting quality aspects using different algorithms (including traditional signal processing methods and state-of-the-art DNNs)

• Project communication and reporting

• Publication and presentation of project and research results in scientific journals, at conferences, and in workshops

2.2. Requirements

• Master or diploma in electrical engineering, computer engineering, computer science, media informatics, media technology, information systems management (or an equivalent technical background)

• Profound knowledge in digital signal processing, beneficial: speech signal processing or audio signal processing, respectively

• Good programming skills (e.g. MATLAB or Python) and safe handling of web development tools (e.g. HTML5/CSS3, JS, ideally also backend development skills)

• Interest in running user studies with test participants to determinate speech quality

• Language skills: English and German fluent in writing and speaking

• Knowledge about empirical studies and statistical data analysis is appreciated

• Joy of working in an interdisciplinary and international environment

Application

For both positions, please send the following documents, bundled in a single PDF file, to Prof. Dr.-Ing. Sebastian Möller bewerbung@qu.tu-berlin.de: Letter of application, curriculum vitae, copies of certificates, job references. Please also specify for which position you are applying.

To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired. Qualified individuals with disabilities will be favored.

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PhD Position : Naver Labs Europe (France) and FBK Trento (Italy) start Nov 2022

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 L'offre de thèse sur 'le Rôle de la conduction osseuse dans le contrôle de la voix (parole, chant) et de l'expression musicale' (Role of bone conducted feedback in the control of voice

(speech, singing) and musical expression) n'a toujours pas été pourvue :
http://www.gipsa-lab.grenoble-inp.fr/transfert/propositions/1_2022-07-05_offretheseINCEPTION.pdf

Si vous souhaitez candidater, aller sur le lien suivant :

Bien cordialement,

Pierre Baraduc, Coriandre Vilain et Nathalie Henrich Bernardoni

***********************************************************************************************************

Offre de thèse sur 'le Rôle de la conduction osseuse dans le contrôle de la voix (parole, chant) et de l'expression musicale' (Role of bone conducted feedback in the control of voice
(speech, singing) and musical expression):
http://www.gipsa-lab.grenoble-inp.fr/transfert/propositions/1_2022-07-05_offretheseINCEPTION.pdf

Pour candidater, aller sur le lien suivant :

https://emploi.cnrs.fr/Offres/Doctorant/UMR5216-CHRROM-018/Default.aspx

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PhD in ML/NLP – Fairness and self-supervised learning for speech processing

Starting date: November 1st, 2022 (flexible)

Application deadline: September 5th, 2022

Interviews (tentative): September 19th, 2022

Salary: ~2000€ gross/month (social security included)

Mission: research oriented (teaching possible but not mandatory)

 

Keywords: speech processing, fairness, bias, self-supervised learning, evaluation metrics 

CONTEXT

The ANR project E-SSL (Efficient Self-Supervised Learning for Inclusive and Innovative Speech Technologies) will start on November 1st 2022. Self-supervised learning (SSL) has recently emerged as one of the most promising artificial intelligence (AI) methods as it becomes now feasible to take advantage of the colossal amounts of existing unlabeled data to significantly improve the performances of various speech processing tasks.

 

PROJECT OBJECTIVES

Speech technologies are widely used in our daily life and are expanding the scope of our action, with decision-making systems, including in critical areas such as health or legal aspects. In these societal applications, the question of the use of these tools raises the issue of the possible discrimination of people according to criteria for which society requires equal treatment, such as gender, origin, religion or disability...  Recently, the machine learning community has been confronted with the need to work on the possible biases of algorithms, and many works have shown that the search for the best performance is not the only goal to pursue [1]. For instance, recent evaluations of ASR systems have shown that performances can vary according to the gender but these variations depend both on  data used for learning and on models [2]. Therefore such systems are increasingly scrutinized for being biased while trustworthy speech technologies definitely represents a crucial expectation.

 Both the question of bias and the concept of fairness have now become important aspects of AI, and we now have to find the right threshold between accuracy and the measure of fairness. Unfortunately, these notions of fairness and bias are challenging to define and their
 meanings can greatly differ [3].

 The goals of this PhD position are threefold:

- First make a survey on the many definitions of robustness, fairness and bias with the aim of coming up with definitions and metrics fit for speech SSL models

- Then gather speech datasets with high amount of well-described metadata

- Setup an evaluation protocol for SSL models and analyzing the results.

 

SKILLS

• Master 2 in Natural Language Processing, Speech Processing, computer science or data science.

• Good mastering of Python programming and deep learning framework.

• Previous experience in bias in machine learning would be a plus

• Very good communication skills in English

• Good command of French would be a plus but is not mandatory 

 

SCIENTIFIC ENVIRONMENT

The PhD position will be co-supervised by Alexandre Allauzen (Dauphine Université PSL, Paris) and Solange Rossato and François Portet (Université Grenoble Alpes). Joint meetings are planned on a regular basis and the student is expected to spend time in both places. Moreover, two other PhD positions are open in this project.  The students, along with the partners will closely collaborate. For instance, specific SSL models along with evaluation criteria will be developed by the other PhD students. Moreover, the PhD student will collaborate with several team members involved in the project in particular the two other PhD candidates who will be recruited  and the partners from LIA, LIG and Dauphine Université PSL, Paris. The means to carry out the PhD will be provided both in terms of missions in France and abroad and in terms of equipment. The candidate will have access to the cluster of GPUs of both the LIG and Dauphine Université PSL. Furthermore, access to the National supercomputer Jean-Zay will enable to run large scale experiments.

 

INSTRUCTIONS FOR APPLYING

Applications must contain: CV + letter/message of motivation + master notes + be ready to provide letter(s) of recommendation; and be addressed to Alexandre Allauzen (alexandre.allauzen@espci.psl.eu), Solange Rossato (Solange.Rossato@imag.fr) and François Portet (francois.Portet@imag.fr). We celebrate diversity and are committed to creating an inclusive environment for all employees.

 

REFERENCES:

[1] Mengesha, Z., Heldreth, C., Lahav, M., Sublewski, J. & Tuennerman, E. “I don’t Think These Devices are Very Culturally Sensitive.”—Impact of Automated Speech Recognition Errors on African Americans. Frontiers in Artificial Intelligence 4. issn: 2624-8212. https://www.frontiersin.org/article/10.3389/frai.2021.725911 (2021).

[2] Garnerin, M., Rossato, S. & Besacier, L. Investigating the Impact  of Gender Representation in ASR Training Data: a Case Study on Librispeech in Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing (2021), 86–92.
[3] Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. & Galstyan, A. A Survey on Bias and Fairness in Machine Learning.  ACMComput. Surv. 54. issn: 0360-0300. https://doi.org/10.1145/3457607 (July 2021).

 

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PhD in ML/NLP – Efficient, Fair, robust and knowledge informed self-supervised learning for speech processing

Starting date: November 1st, 2022 (flexible)

Application deadline: September 5th, 2022

Interviews (tentative): September 19th, 2022

Salary: ~2000€ gross/month (social security included)

Mission: research oriented (teaching possible but not mandatory)

 

Keywords: speech processing, natural language processing, self-supervised learning, knowledge informed learning, Robustness, fairness

 

CONTEXT

The ANR project E-SSL (Efficient Self-Supervised Learning for Inclusive and Innovative Speech Technologies) will start on November 1st 2022. Self-supervised learning (SSL) has recently emerged as one of the most promising artificial intelligence (AI) methods as it becomes now feasible to take advantage of the colossal amounts of existing unlabeled data to significantly improve the performances of various speech processing tasks.

 

PROJECT OBJECTIVES

Recent SSL models for speech such as HuBERT or wav2vec 2.0 have shown an impressive impact on downstream tasks performance. This is mainly due to their ability to benefit from a large amount of data at the cost of a tremendous carbon footprint rather than improving the efficiency of the learning. Another question related to SSL models is their unpredictable results once applied to realistic scenarios which exhibit their lack of robustness. Furthermore, as for any pre-trained models applied in society, it is important to be able to measure the bias of such models since they can augment social unfairness.

 

The goals of this PhD position are threefold:

- to design new evaluation metrics for SSL of speech models ;

- to develop knowledge-driven SSL algorithms ;

- to propose methods for learning robust and unbiased representations.  

 

SSL models are evaluated with downstream task-dependent metrics e.g., word error rate for speech recognition. This couple the evaluation of the universality of SSL representations to a potentially biased and costly fine-tuning that also hides the efficiency information related to the pre-training cost. In practice, we will seek to measure the training efficiency as the ratio between the amount of data, computation and memory needed to observe a certain gain in terms of performance on a metric of interest i.e., downstream dependent or not. The first step will be to document standard markers that can be used as robust measurements to assess these values robustly at training time. Potential candidates are, for instance, floating point operations for computational intensity, number of neural parameters coupled with precision for storage, online measurement of memory consumption for training and cumulative input sequence length for data.

 

Most state-of-the-art SSL models for speech rely on masked prediction e.g. HuBERT and WavLM, or contrastive losses e.g. wav2vec 2.0. Such prevalence in the literature is mostly  linked to the size, amount of data and computational resources injected by the company producing these models. In fact, vanilla masking approaches and contrastive losses may be identified as uninformed solutions as they do not benefit from in-domain expertise. For instance, it has been demonstrated that blindly masking frames in the input signal i.e. HuBERT and WavLM results in much worse downstream performance than applying unsupervised phonetic boundaries [Yue2021] to generate informed masks. Recently some studies have demonstrated the superiority of an informed multitask learning strategy carefully selecting self-supervised pretext-tasks with respect to a set of downstream tasks, over the vanilla wav2vec 2.0 contrastive learning loss [Zaiem2022]. In this PhD project, our objective is: 1. continue to develop knowledge-driven SSL algorithms reaching higher efficiency ratios and results at the convergence, data consumption and downstream performance levels; and 2. scale these novel approaches to a point enabling the comparison with current state-of-the-art systems and therefore motivating a paradigm change in SSL for the wider speech community.

 

Despite remarkable performance on academic benchmarks, SSL powered technologies e.g. speech and speaker recognition, speech synthesis and many others may exhibit highly unpredictable results once applied to realistic scenarios. This can translate into a global accuracy drop due to a lack of robustness to adversarial acoustic conditions, or biased and discriminatory behaviors with respect to different pools of end users. Documenting and facilitating the control of such aspects prior to the deployment of SSL models into the real-life is necessary for the industrial market. To evaluate such aspects, within the project, we will create novel robustness regularization and debasing techniques along two axes: 1. debasing and regularizing speech representations at the SSL level; 2. debasing and regularizing downstream-adapted models (e.g. using a pre-trained model).

 

To ensure the creation of fair and robust SSL pre-trained models, we propose to act both at the optimization and data levels following some of our previous work on adversarial protected attribute disentanglement and the NLP literature on data sampling and augmentation [Noé2021]. Here, we wish to extend this technique to more complex SSL architectures and more realistic conditions by increasing the disentanglement complexity i.e. the sex attribute studied in [Noé2021] is particularly discriminatory. Then, and to benefit from the expert knowledge induced by the scope of the task of interest, we will build on a recent introduction of task-dependent counterfactual equal odds criteria [Sari2021] to minimize the downstream performance gap observed in between different individuals of certain protected attributes and to maximize the overall accuracy. Following this multi-objective optimization scheme, we will then inject further identified constraints as inspired by previous NLP work [Zhao2017]. Intuitively, constraints are injected so the predictions are calibrated towards a desired distribution i.e. unbiased.

 

SKILLS

• Master 2 in Natural Language Processing, Speech Processing, computer science or data science.

• Good mastering of  Python programming and  deep learning framework.

• Previous in Self-Supervised Learning, acoustic modeling or ASR would be a plus

• Very good communication skills in English

• Good command of French would be a plus but is not mandatory 

 

SCIENTIFIC ENVIRONMENT

 

The thesis will be conducted within the Getalp teams of the LIG laboratory (https://lig-getalp.imag.fr/) and the LIA laboratory (https://lia.univ-avignon.fr/). The GETALP team and the LIA have a strong expertise and track record in Natural Language Processing and speech processing. The recruited person will be welcomed within the teams which offer a stimulating, multinational and pleasant working environment.

The means to carry out the PhD will be provided both in terms of missions in France and abroad and in terms of equipment. The candidate will have access to the cluster of GPUs of both the LIG and LIA. Furthermore, access to the National supercomputer Jean-Zay will enable to run large scale experiments.

The PhD position will be co-supervised by Mickael Rouvier (LIA, Avignon) and Benjamin Lecouteux and François Portet (Université Grenoble Alpes). Joint meetings are planned on a regular basis and the student is expected to spend time in both places. Moreover, the PhD student will collaborate with several team members involved in the project in particular the two other PhD candidates who will be recruited  and the partners from LIA, LIG and Dauphine Université PSL, Paris. Furthermore, the project will involve one of the founders of SpeechBrain, Titouan Parcollet with whom the candidate will interact closely.

 

 

INSTRUCTIONS FOR APPLYING

Applications must contain: CV + letter/message of motivation + master notes + be ready to provide letter(s) of recommendation; and be addressed to Mickael Rouvier (mickael.rouvier@univ-avignon.fr), Benjamin Lecouteux (benjamin.lecouteux@univ-grenoble-alpes.fr) and François Portet (francois.Portet@imag.fr). We celebrate diversity and are committed to creating an inclusive environment for all employees.

 

REFERENCES:

[Noé2021] Noé, P.- G., Mohammadamini, M., Matrouf, D., Parcollet, T., Nautsch, A. & Bonastre, J.- F. Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy Preservation in Proc. Interspeech 2021 (2021), 1902–1906.

[Sari2021] Sarı, L., Hasegawa-Johnson, M. & Yoo, C. D. Counterfactually Fair Automatic Speech Recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing 29, 3515–3525 (2021)

[Yue2021] Yue, X. & Li, H. Phonetically Motivated Self-Supervised Speech Representation Learning in Proc. Interspeech 2021 (2021), 746–750.

[Zaiem2022]  Zaiem, S., Parcollet, T. & Essid, S. Pretext Tasks Selection for Multitask Self-Supervised Speech Representation in AAAI, The 2nd Workshop on Self-supervised Learning for Audio and Speech Processing, 2023 (2022).

[Zhao2017] Zhao, J., Wang, T., Yatskar, M., Ordonez, V. & Chang, K. - W. Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (2017), 2979–2989.

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The Grenoble Alps University offers a PostDoc position for a highly motivated candidate to be working on the multi-disciplinary research project THERADIA, which aims to create an empathic virtual assistant that accompanies cognitively impaired patients during remediation exercises at home. The successful candidate will have the exciting opportunity to develop new machine learning techniques for the robust detection of affective and cognitive behaviours from newly collected audiovisual data. Models will be incorporated into the virtual agent to tailor the interaction with the patient, using specific interaction scenarios, and these models will be evaluated and fine-tuned in a clinical trial to demonstrate the effectiveness of the agent in supporting patients suffering from cognitive conditions during digital therapies. If successful, the system will be operated nationally and the cognitive remediation sessions will be covered by social security.

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 In the framework of the European/Japanese e-VITA project (https://www.e-vita.coach/), IMT Atlantique is

offering a 15-month post-doctoral position in the field of active living technologies (IoT, data fusion, AI,

cloud/edge architectures, user services, coaching, NLP, etc.).

Description and link to apply:
 https://institutminestelecom.recruitee.com/l/en/o/postdoctorante-ou-postdoctorant-en-fusion-de-donnees-multimodales-cdd-15-mois

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Research Assistant for Conversational Speech Quality Assessment and Prediction - salary grade E 13 TV-L Berliner Hochschulen

part-time employment may be possible

Hire Date

The start date for the position is planned for November 1st, 2022, qualification goal doctorate. It is limited to a duration until September 30th, 2025 and a subsequent ongoing employment is supported if sufficient third-party-funding is available.

About Us

The majority of systems and services that are provided by computer science, electrical engineering and information technology finally are directed to their human users. To successfully build such systems and services, it is essential to investigate and understand users and their behavior when interacting with technology.From this, design principles for human-machine interfaces can be derived and requirements for the underlying technologies can be defined.

The Quality and Usability Lab is part of TU Berlin’s Faculty IV and deals with the design and evaluation of human-machine interaction, in which aspects of human perception and communication, technical systems and the design of interaction are the subject of our research. We focus on self-determined work in an interdisciplinary and international team; for this we offer open and flexible working conditions that promote scientific and personal exchange and are a prerequisite for excellent results.

Tasks

The position is open to do research in the fieldof speech signals analysis, and the assessment of speech quality in different (mobile and fixed) networks. Therefore, speech signalsare to be analyzed in listening-only as well as conversational situations in order to get indications or the perceived quality. Based on these analysis, signal-based and parametric models for the estimation of speech quality can be extended and integrated. One focus of the present research may be the evaluation of new speech codecs in different network scenarios. The models are to be validated based on subjective listening and conversation tests. For this purpose, methods of crowdsourcing can be applied, i. e. real users should carry out the data collection and/or evaluation via an online platform. Scientifically interesting is the comparison of such crowdsourced data to those that can be obtained under laboratory conditions.

The outcomes of the research should be used to support the preparation of a new project application, and may also become a foundation for a later PhD thesis. A subsequent position as a research assistant from the project funds would be possible if the funds were approved.

The concrete tasks include, among other things:

Maintaining and further developing a platform to conduct web-based voice calls

• Conducting subjective conversation tests in the laboratory and via crowdsourcing

• Analysis of speech signals

• Creating and evaluating models for predicting quality aspects using different algorithms (including traditional signal processing methods and state-of-the-art DNNs)

• Project communication and reporting

• Publication and presentation of project and research results in scientific journals, at conferences, and in workshops

Requirements

• Master or diploma in electrical engineering, computer engineering, computer science, media informatics, media technology, information systems management (or an equivalent technical background)

• Profound knowledge in digital signal processing, beneficial: speech signal processing or audio signal processing, respectively

• Good programming skills (e.g. MATLAB or Python) and safe handling of web development tools (e.g. HTML5/CSS3, JS, ideally also backend development skills)

• Interest in running user studies with test participants to determinate speech quality

• Language skills: English and German fluent in writing and speaking

• Knowledge about empirical studies and statistical data analysis is appreciated

• Joy of working in an interdisciplinary and international environment

Compensation

“Tarifvertrag für den öffentlichen Dienst der Länder (TV-L)” (E13, 100%).

Application

Please send the following documents, bundled in a single PDF file, to
Prof. Dr.-Ing. Sebastian Möller bewerbung@qu.tu-berlin.de:

Letter of application, curriculum vitae, copies of certificates, job references.

To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired. Qualified individuals with disabilities will be favored. The TU Berlin values the diversity of its members and is committed to the goals of equal opportunities.

Please send electronic copies only. Original documents will not be returned.

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Research Assistant for Chatbot-based Support for Self-organization During Studies - salary grade E 13 TV-L Berliner Hochschulen

part-time employment may be possible

Hire Date

The start date for the position is planned for October 1st, 2022, with the qualification goal doctorate. It is limited to a duration of 26 months and a subsequent ongoing employment is supported if the PhD cannot be finished in the given time.

About Us

Most systems and services that are provided by computer science, electrical engineering and information technology finally are oriented on the needs of their human users. To build successfully build such systems and services it essential to investigate and understand users and their behavior when interacting with technology.From this, design principles for human-machine interfaces can be derived and requirements for the underlying technologies can be defined.

The Quality and Usability Lab is part of TU Berlin’s Faculty IV and deals with the design and evaluation of human-machine interaction, in which aspects of human perception, technical systems and the design of interaction are the subject of our research. We focus on self-determined work in an interdisciplinary and international team; for this we offer open and flexible working conditions that promote scientific and personal exchange and are a prerequisite for excellent results.

Tasks

Conception and development of text-based interactive dialogue systems, so-called chatbots, as part of the USOS project (chatbot-based support for self-organization during studies). Machine learning methods are used both to process text-based information and to control dialogs. The range of tasks also includes the design and implementation of graphic user interfaces, e.g., as a web app, Android app or iOS app. The quality and the user experience of the created interaction concepts are then evaluated in the context of user studies.

The specific tasks include:

• Implementation of information extraction for the module transfer system and course catalog of the TU Berlin

• Implementation of natural language understanding, dialog management and response generation for a chatbot

• Communication with project participants on the technical requirements of the chatbot

• Planning and conducting user studies

• Active participation in the conception, construction, and evaluation of the overall system

• Publication and presentation of project and research results in scientific journals, at conferences, and in workshops as well as standardization meetings of ITU-T

Professionally experienced employees from our team support you with self-motivated familiarization with the areas of responsibility.

Requirements

• Master or diploma in electrical engineering, computer engineering/science, informatics, media informatics, media technology, information systems (or an equivalent technical background)

• Ability to work independently in a team and good self-organization

• Very good programming knowledge in Python and its routine use in development environments and experience with working under Linux and the command line

• Experience in the use of machine learning frameworks such as Tensorflow, Keras, or PyTorch

• In-depth knowledge of the principles of machine learning (supervised learning, unsupervised learning and reinforcement learning)

• Previous experience in one of the following areas: Information Extraction, Natural Language Understanding, Natural Language Generation

• Desired previous knowledge (not required)

  • Experience in the preparation and efficient processing of training data for AI-based systems

  • Experience in the development of chatbots or speech dialog systems

  • Experience with transformer-based language models such as BERT or GPT

  • Experience with empirical research and statistical data analysis

• Interest in carrying out experiments with test subjects to determine quality and user experience

• Language skills: German fluent in writing and speaking, English communication secure

• Joy of working in an interdisciplinary and international environment

Compensation

“Tarifvertrag für den öffentlichen Dienst der Länder (TV-L)” (E13, 100%)

Application

Please send the following documents, bundled in a single PDF file, to
Prof. Dr.-Ing. Sebastian Möller bewerbung@qu.tu-berlin.de:

Letter of application, curriculum vitae, copies of certificates, job references.

To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired.

Qualified individuals with disabilities will be favored.

Please send copies only. Original documents will not be returned.

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Research Assistant for Multimodal Interactive Assistance for the Digital Collection of Patient-Reported Outcome Measures - salary grade E 13 TV-L Berliner Hochschulen

part-time employment may be possible

Hire Date

The start date for the position is planned for December 1st, 2022, with the qualification goal doctorate. It is limited to a duration of 2.5 years and a subsequent ongoing employment is supported if the PhD cannot be finished in the given time.

About Us

Most systems and services that are provided by computer science, electrical engineering and information technology finally are oriented on the needs of their human users. To build successfully build such systems and services it essential to investigate and understand users and their behavior when interacting with technology.From this, design principles for human-machine interfaces can be derived and requirements for the underlying technologies can be defined.

The Quality and Usability Lab (https://tu.berlin/qu) is part of TU Berlin’s Faculty IV and deals with the design and evaluation of human-machine interaction, in which aspects of human perception, technical systems and the design of interaction are the subject of our research. We focus on self-determined work in an interdisciplinary and international team; for this we offer open and flexible working conditions that promote scientific and personal exchange and are a prerequisite for excellent results.

Tasks

Conception and development of an interactive natural language-based dialog system, as part of the project MIA-PROM (Multimodal interactive assistance for the digital collection of Patient-Reported Outcome Measures). The project is in the field of outpatient rehabilitation and requires cooperation with researchers (human-machine interaction, technology-sociology, and healthcare) and the intended users. In the subproject Adaptive Dialog, methods of machine learning are used both for the processing of natural language utterances and for the control and adaptation of dialogs. The range of tasks also includes research on the methods and interaction concepts used in the subproject. The quality and user experience of the created interaction concepts are then evaluated in empirical user studies.

The specific tasks include:

• Implementation of components of a spoken dialog system, in particular, natural language understanding, dialog management and response generation

• Communication with project partners on the technical and functional requirements of the dialog system

• Planning and implementation of user studies

• Active participation in the conception, implementation, and evaluation of the overall system

• Publication and presentation of project and research results in scientific journals, at conferences and workshops, as well as in international standardization committees

Professionally experienced employees from our team support you with self-motivated familiarization with the areas of responsibility.

Requirements

• Master or diploma in electrical engineering, computer engineering/science, informatics, media informatics, media technology, information systems (or an equivalent technical background)

• Ability to work independently in a team and good self-organization

• Good programming skills in Python and their routine use in development environments

• Previous experience in the field of Natural Language Processing

• Experience in the use of frameworks for Natural Language Processing (e.g., Rasa NLU, AllenNLP or SparkNLP)

• Fundamental knowledge of machine learning principles

• Optional previous knowledge (not required):

• Experience in the preparation and efficient processing of training data for AI-based systems

• Experience in the development of chatbots or voice dialogue systems

• Experience with Transformer-based language models such as BERT or GPT

• Interest in conducting empirical studies with human participants to determine quality and user experience in human-machine interaction

• Language skills: German fluent in writing and speaking, English communication secure

• Desire to work in an interdisciplinary and international environment

Compensation

“Tarifvertrag für den öffentlichen Dienst der Länder (TV-L)” (E13, 100%)

Application

Please send the following documents, bundled in a single PDF file, to
Prof. Dr.-Ing. Sebastian Möller bewerbung@qu.tu-berlin.de:

Letter of application, curriculum vitae, copies of certificates, job references.

To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired.

Qualified individuals with disabilities will be favored.

Please send copies only. Original documents will not be returned.

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A successful candidate has strong expertise in 

 a)speech and/or language technology or in

b) childlanguage
development research with quantitative methods (e.g., developmental psychology,psycholinguistics,
cognitive science).

Fluent programming (at least Python,Matlab, or R) and oral and written English skills are
required. Strong motivation towards understanding the underpinnings of human language learning and
processing is a must. Experience from computational modelling or use of statistical models in empirical
research are considered as an advantage.
Potential candidates must be capable of carrying out independent academic research at the highest
international level.Competence must be demonstrated through several existing publications in
internationally recognised peer-reviewed journals and conferences.
We offer
The position will be filled for a fixed-term period of up to 3.5 years, but is negotiated according to
applicant’s career plans. Starting date is also negotiable, but should not be later than March2023.  A trial
period of 6 months is applied to all new employees.
We offer competitive academic salary, typically between 3500–3600€ per month for astarting postdoc,
generally depending on experience and merits achieved (the position is placed on job demand levels 5–6 in
accordance withthe Finnish University Salary System).The position also includespossibilities for short-term
researcher mobility to other international research labs.Traveling costs to presenting peer-reviewed work
in major international conferences are covered by default. In addition, the position comes with extensive
benefits such as occupational healthcare, on-campus sports facilities, flexible working hours, and several
restaurants and cafés on the campus with staff discounts. The jobis associated with1612 hannual working
time, which translates to approx. 6 weeks of holiday per year.
How to apply
Send the application through the online portalat
https://tuni.rekrytointi.com/paikat/?o=A_RJ&jgid=1&jid=1572.
Deadline for applications is 9st of October2022 at 23.59(GMT+3). Note that we may start interviewing
applicants already before the deadline. We reserve the opportunity to decide not to fill the position in
case a suitable candidate is not found during the process.
The application should contain the following documents (all in .pdfformat):
-A free-form letter of motivation for the position in question(max.1page)
-Academic CV with contact information
-A complete list of publications
-A copy of doctoral degree certificate
-Potential letters of recommendation(max.3)
Please name all the documents as surname_CV.pdf,surname_list_of_publications.pdf... etc. Only the
applications sent through the university application portal and containing the requested attachments in the
instructed format will be considered in the recruitment process.
The most promising candidates will be interviewed in person before the final decision.
For more information about the position, please contact Associate Professor Okko Räsänen
(firstname.surname@tuni.fi; no umlauts) by email. For more information on our group activities and recent
publications, see https://webpages.tuni.fi/specog/index.html.

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Le LISN recrute un post-doc d’un an dans le cadre du projet ANR GEM (Gender Equality Monitor) sur l’identification d'expressions genrées par des représentations vectorielles sur un corpus de transcription de la parole dans les médias. 

l’offre est détaillée ci-dessous :

Post-doctorat (H/F) Identification d'expressions genrées par des représentations vectorielles sur un corpus de transcription de la parole dans les médias

Informations générales

Référence : UMR9015-CYRGRO-002

Lieu de travail : ST AUBIN

Date de publication : samedi 10 septembre 2022

Type de contrat : CDD Scientifique

Durée du contrat : 12 mois

Date d'embauche prévue : 1 décembre 2022

Quotité de travail : Temps complet

Rémunération : Entre 2889,91€ et 4082,9€ bruts mensuels selon expérience

Niveau d'études souhaité : Doctorat

Expérience souhaitée : 1 à 4 années

Missions

Le projet GEM (Gender Equality Monitor) vise à analyser les interactions entre femmes et hommes dans les médias (radio et télévision), et plus particulièrement les différences de représentations selon que la personne qui s'exprime est une femme ou un homme, selon son rôle (anonyme, journaliste, politique, etc.), et selon les thèmes abordés. Dans ce projet inter-disciplinaire, les partenaires informatiques (dont le LISN) ont pour mission d'implémenter les descripteurs qui permettront aux partenaires en sciences humaines et sociales de quantifier et qualifier les différences de représentation.

https://anr.fr/Projet-ANR-19-CE38-0012

Activités

La personne recrutée (H/F) aura en charge de mettre au point des techniques de traitement automatique des langues (TAL) non supervisées ou semi-supervisées appliquées à des corpus de transcriptions automatiques de la parole, pour identifier les 'expressions genrées' telles que les références à des stéréotypes culturels en fonction du genre, les entités nommées traditionnelles ou toute référence à la vie privée, l'âge, le physique, la sexualité, les compétences, etc.

De manière secondaire, l'analyse des biais dans les modèles de langue pourra également être conduite.

Les corpus sont mis à disposition par le porteur du projet (Institut National de l'Audiovisuel) et se composent : de matinales radios et journaux de télévision du corpus GMMP (Global Monitoring Media Project), d'émissions de radio françaises (émissions culinaires, économiques, sportives, et libre-antennes) pour l'étude des incivilités (interruptions, injures, etc.), et d'émissions de télé-réalité (Loft Story 2001, Les Marseillais à Dubaï 2021). Aucune annotation n'est disponible autour des expressions genrées. La personne recrutée devra donc privilégier des méthodes non supervisées ou semi-supervisées.

Ce travail sera co-encadré par Mme Sahar Ghannay (MCF en informatique à l'Université Paris Saclay) et M. Cyril Grouin (IR en informatique au CNRS). Le contrat sera financé par l'Agence Nationale de la Recherche (ANR GEM 2019) porté par David Doukhan (Institut National de l'Audiovisuel).

Compétences

- très bonne maîtrise du français

- traitement automatique des langues et de la parole ; une formation spécifique dans cette discipline est un plus

- expérience des plongements lexicaux et réseaux de neurones

Contexte de travail

Le Laboratoire Interdisciplinaire des Sciences du Numériques (LISN) est une unité installée sur le plateau de Saclay et créée en 2021 de la fusion des laboratoires LIMSI et LRI. Les recherches effectuées au LISN couvrent un large spectre scientifique et sont reconnues à l'international.

Le laboratoire comprend plus de 380 membres répartis dans 16 équipes de recherche et 6 services de support et soutien. Les locaux sont intégralement en zone à régime restrictif (ZRR).

La personne recrutée travaillera au sein de l'équipe ILES, en lien étroit avec les chercheurs des équipes ILES et TLP impliqués sur le projet, au sein du département Sciences et Technologies des Langues (STL).

Contraintes et risques

Déplacement possible en Ile-de-France pour les réunions de travail ponctuelle

Déplacements nationaux et internationaux en conférence en cas d'article à présenter

Travail sur ordinateur

Candidature ici: https://emploi.cnrs.fr/Offres/CDD/UMR9015-CYRGRO-002/Default.aspx

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