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ISCApad #305 |
| Friday, November 10, 2023 by Chris Wellekens |
6 Jobs
| 6-1 | (2023-05-09) PhD position @ IMT Brest France and Instituto Superior Técnico Lisbon, Portugal PhD Title: SUMMA-Sound : SUMMarization of Activities of daily living using Sound-based activity recognition Partnership: IMT Atlantique : Campus ☒ Brest ☐ Nantes ☐ Rennes Laboratory : Lab-STICC Doctoral school : ☒ SPIN ☐ 3MG Funding: IMT Atlantique, co-tutelle with Instituto Superior Técnico Context : IMT Atlantique, internationally recognised for the quality of its research, is a leading general engineering school under the aegis of the French Ministry of Industry and Digital Technology, ranked in the three main international rankings (THE, SHANGHAI, QS). Located on three campuses, Brest, Nantes and Rennes, IMT Atlantique aims to combine digital technology and energy to transform society and industry through training, research and innovation. It aims to be the leading French higher education and research institution in this field on an international scale. With 290 researchers and permanent lecturers, 1000 publications and 18 M€ of contracts, it supervises 2300 students each year and its training courses are based on cutting-edge research carried out within 6 joint research units: GEPEA, IRISA, LATIM, LABSTICC, LS2N and SUBATECH. The proposed thesis is part of the research activities of the team RAMBO (Robot interaction, Ambient systems, Machine learning, Behaviour, Optimization) and of the laboratory Lab-STICC and the department of Computer Science of IMT Atlantique. Scientific context: The objective of this thesis is to develop a method for collecting and summarizing domestic health-related data relevant for medical diagnosis, in a non-intrusive manner using audio information. This research addresses the lack of existing practical tools for providing high-level succinct information to medical staff on the evolution of patients they follow for health diagnostic purposes. This research is based on the assumption that valuable diagnostic data can be collected by observing short- and long-term lifestyle changes and behavioural anomalies. It relies on the latest advances in the domains of audio-based activity recognition, summarization of human activity, and health diagnosis. Research on health diagnosis in domestic environments has already explored a variety of sensors and modalities for gathering data on human health indicators [5]. Nevertheless, audio-based activity recognition is notable for its less intrusive nature. Employing state-of-the-art sound-based activity recognition models [2] to monitor domestic human activity, the thesis will investigate and develop methods for summarization of human activity [3] in a human-understandable language, in order to produce easily interpretable data by doctors who, remotely, monitor their patients [4]. This work continues and fosters the research of the RAMBO team at IMT Atlantique on ambient systems, enabling well ageing at home for the elderly adults or dependent populations [1]. We expect this thesis to provide technology likely to relieve the burden on gerontologists and elderly-care facilities, and alleviate the caregiver shortage by offering some automatic support to the task of monitoring elderly or handicapped people, enabling them to age-at-home while still being followed by medical specialists using automated means. Expected contributions of the thesis Scientific goals: (1) Determine the set of human activities relevant for health diagnosis, (2) Implement a state-of-the-art model for audio-based activity recognition and validate its function by clinicians, (3) Develop a model for summarizing the evolution of human activity over time intervals of arbitrary duration (typically spanning from days to months and possibly years). Expected outcomes of the PhD: (1) A model for semantic summarization of human activity, based on sound recognition of activities of daily living. (2) A proof of concept for this model Candidate profile and required skills: • Master Degree in Computer Science (or equivalent) • Programming and Software Engineering skills (Python, Git, Software Architecture Design) • Data science skills • Machine learning skills • English speaking and writing skills References: [1] Damien Bouchabou. “Human activity recognition in smart homes : tackling data variability using context-dependent deep learning, transfer learning and data synthesis”. Theses. Ecole nationale supérieure Mines-Télécom Atlantique, May 2022. url: https://theses.hal.science/tel-03728064. [2] Detection and Classification of Acoustic Scenes and Events (DCASE). url: https://dcase.community/challenge2022/task-soundevent-detection-in-domestic-environments (visited on 07/01/2022). [3] P Durga et al. “When less is better: A summarization technique that enhances clinical effectiveness of data”. In: Proceedings of the 2018 International Conference on Digital Health. 2018, pp. 116–120. [4] Akshay Jain et al. “Linguistic summarization of in-home sensor data”. In: Journal of Biomedical Informatics 96 (2019), p. 103240. issn: 1532-0464. [5] Mostafa Haghi Kashani et al. “A systematic review of IoT in healthcare: Applications, techniques, and trends”. In: Journal of Network and Computer Applications 192 (2021), p. 103164. Work Plan: The thesis will be organised in the following steps: (1) Definition of pertinent sounds and activities for health diagnosis, (2) Hardware set-up, (3) Dataset constitution, (4) Activity recognition, (5) Diarization of activities, (6) Summarization, (7) Validation in a real environment. Application: To apply for this position, please send an email with your Curriculum Vitae, a document with your academic results (if possible), and a couple of lines describing your motivation to pursue a PhD to mihai[dot]andries[at]imt-atlantique[dot]fr before 16 May 2023. Additional Information : Application deadline : 16 May 2023 Start date : Fall 2023 Contract duration: 36 months Localisation - Location : Brest (France) and Lisbon (Portugal) Contact(s) : Mihai ANDRIES (mihai[dot]andries[at]imt-atlantique.fr) Plinio Moreno (plinio[at]isr.tecnico.ulisboa.pt)
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| 6-2 | (2023-05-11) PhD position @ ISIR and IRCAM Paris France
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| 6-3 | (2023-05-11) PhD-student position in artificial intelligence, human-robot interaction @ KTH, Stockholm, Sweden We are looking for a PhD student who is interested in Artificial Intelligence, Machine Learning, Natural Language Processing and Human-Robot Interaction. The doctoral student will work in a newly funded project at the Department of Speech, Music and Hearing within the School of Electrical Engineering and Computer Science at KTH. The project is financed by the Swedish AI-program WASP (Wallenberg AI, Autonomous Systems and Software Program) which offers a graduate school with research visits, partner universities, and visiting lecturers.
The newly started project is titled 'Anticipatory Control in Conversational Human-Robot Interaction'. The aim of the project is to use self-supervised learning to develop generic language models for human-robot interaction and explore how such models can be used in real time to predict and anticipate human behavior and thereby improve the interaction. Whereas traditional language models in NLP (such as BERT, GPT) have focused on written language, we want to model multi-modal conversation, where aspects such as engagement, turn-taking, and incremental processing are of importance. This means that the models will have to process both text, audio and video, including aspects such as how the human users move and their facial expressions. In collaboration with industry and other projects, we will then explore how such models can be used for social robotic applications. Another important focus will be on model analysis and visualization.
For more information about the position, see https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:623390/where:4/
If you have any questions, don’t hesitate to contact Prof. Gabriel Skantze (skantze@kth.se)
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| 6-4 | (2023-05-15) Post-doctoral and engineer positions@ LORIA-INRIA, Nancy, France Automatic speech recognition for non-natives speakers in a noisy environment
Post-doctoral and engineer positions
Starting date: July-September of 2023
Duration: 24 months for a post-doc position and 12 months for an engineer position
Supervisors: Irina Illina, Associate Professor, HDR Lorraine University LORIA-INRIA Multispeech Team, illina@loria.fr
Emmanuel Vincent, Senion Research Scientist & Head of Science, INRIA Multispeech Team, emmanuel.vincent@inria.fr
http://members.loria.fr/evincent/Cons: the application must meet the requirements of the French Directorate General of Armament (Direction générale de l'armement, DGA).
Context
When a person has their hands busy performing a task like driving a car or piloting an airplane, voice is a fast and efficient way to achieve interaction. In aeronautical communications, the English language is most often compulsory. Unfortunately, a large part of the pilots are not native English and speak with an accent dependent on their native language and are therefore influenced by the pronunciation mechanisms of this language. Inside an aircraft cockpit, non-native voice of the pilots and the surrounding noises are the most difficult challenges to overcome in order to have efficient automatic speech recognition (ASR). The problems of non-native speech are numerous: incorrect or approximate pronunciations, errors of agreement in gender and number, use of non-existent words, missing articles, grammatically incorrect sentences, etc. The acoustic environment adds a disturbing component to the speech signal. Much of the success of speech recognition relies on the ability to take into account different accents and ambient noises into the models used by ARP.
Automatic speech recognition has made great progress thanks to the spectacular development of deep learning. In recent years, end-to-end automatic speech recognition, which directly optimizes the probability of the output character sequence based on the input acoustic characteristics, has made great progress [Chan et al., 2016; Baevski et al., 2020; Gulati, et al., 2020].
Objectives
The recruited person will have to develop methodologies and tools to obtain high-performance non-native automatic speech recognition in the aeronautical context and more specifically in a (noisy) aircraft cockpit.
This project will be based on an end-to-end automatic speech recognition system [Shi et al., 2021] using wav2vec 2.0 [Baevski et al., 2020]. This model is one of the most efficient of the current state of the art. This wav2vec 2.0 model enables self-supervised learning of representations from raw audio data (without transcription).
How to apply: Interested candidates are encouraged to contact Irina Illina (illina@loria.fr) with the required documents (CV, transcripts, motivation letter, and recommendation letters).
Requirements & skills:
- Ph.D. degree in speech/audio processing, computer vision, machine learning, or in a related field,
- ability to work independently as well as in a team,
- solid programming skills (Python, PyTorch), and deep learning knowledge,
- good level of written and spoken English.
References
[Baevski et al., 2020] A. Baevski, H. Zhou, A. Mohamed, and M. Auli. Wav2vec 2.0: A framework for self-supervised learning of speech representations, 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2020.
[Chan et al., 2016] W. Chan, N. Jaitly, Q. Le and O. Vinyals. Listen, attend and spell: A neural network for large vocabulary conversational speech recognition. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, pp. 4960-4964, 2016.
[Chorowski et al., 2017] J. Chorowski, N. Jaitly. Towards better decoding and language model integration in sequence to sequence models. Interspeech, 2017.
[Houlsby et al., 2019] N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, S. Gelly. Parameter-efficient transfer learning for NLP. International Conference on Machine Learning, PMLR, pp. 2790–2799, 2019.
[Gulati et al., 2020] A. Gulati, J. Qin, C.-C. Chiu, N. Parmar, Y. Zhang, J. Yu, W. Han, S. Wang, Z. Zhang, Y. Wu, and R. Pang. Conformer: Convolution-augmented transformer for speech recognition. Interspeech, 2020.
[Shi et al., 2021] X. Shi, F. Yu, Y. Lu, Y. Liang, Q. Feng, D. Wang, Y. Qian, and L. Xie. The accented english speech recognition challenge 2020: open datasets, tracks, baselines, results and methods. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6918–6922, 2021.
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| 6-5 | (2023-05-18) PhD position @ GIPSA Lab, Grenoble, France Nous proposons une offre de thèse en acoustique-aérodynamique-mécatronique de la parole, dans le cadre du projet ANR AVATARS (« Artificial Voice production: control of bio-inspired port-HAmilToniAn numeRical and mechatronic modelS », 2023-2027). Le sujet porte sur la 'Caractérisation du comportement vocal humain dans la parole et dans le chant sur banc mécatronique robotisé. Application au développement de plis vocaux biomimétiques.' Pour plus d'information et pour candidater :
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| 6-6 | (2023-05-20) Poste d' enseignant chercheur, Grenoble, France Nous recherchons pour l'année 2023-2024 une personne pour un CDD 50% enseignement et
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| 6-7 | (2023-05-22) PhD position @Lille and Grenoble, France Nous recherchons un·e candidat·e pour une thèse sur la modélisation computationnelle du
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| 6-8 | (2023-05-22) PhD Causal Machine Learning Applied to NLP and the Study of Large Language Models, Grenoble, France Job Offer: PhD Causal Machine Learning Applied to NLP and the Study of Large Language
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| 6-9 | (2023-05-27) PhD position@ EECS-KTH, Stockholm, Sweden The School of Electrical Engineering and Computer Science (EECS) at the KTH Royal Institute of Technology has an open Ph.D position in Social Robotics at the division of Robotics, Perception and Learning (RPL).
ABOUT KTH
KTH Royal Institute of Technology in Stockholm has grown to become one of Europe’s leading technical and engineering universities, as well as a key center of intellectual talent and innovation. We are Sweden’s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as in architecture, industrial management, urban planning, history and philosophy.
PROJECT DESCRIPTION
This project addresses the challenge of how to enable robots to learn in a scalable and cost-efficient manner by gradually acquiring new knowledge from non-expert, semi-situated teachers. To achieve this, computational methods will be developed for robots to query the semi-situated teachers (e.g. crowd workers) and incorporate the newly acquired knowledge into their existing decision-making to further use in situ. This project is funded by the Swedish Foundation for Strategic Research.
The starting date for the positions is flexible, but preferably during the fall of 2023.
QUALIFICATIONS
The candidate must have a degree in Computer Science or related fields. Documented written and spoken English and programming skills are required. Experience with robotics, human-robot interaction, human-computer interaction, multimodal interaction and machine learning are important.
HOW TO APPLY
The application should include:
1. Curriculum vitae.
2. Transcripts from University/College.
3. Brief description of why the applicant wishes to become a doctoral student.
The application documents should be uploaded using the KTH's recruitment system. More information here:
The application deadline is ** June 2, 2023 **
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| 6-10 | (2023-05-28) Ph.D. position: Automatic speech recognition for non-natives speakers in a noisy environment, LORIA-INRIA, Nancy, France Automatic speech recognition for non-natives speakers in a noisy environment
Ph.D. position
Starting date: September-October 2023
Duration: 36 months
Supervisors: Irina Illina, Associate Professor, HDR Lorraine University LORIA-INRIA Multispeech Team, illina@loria.fr
Emmanuel Vincent, Senior Research Scientist & Head of Science, INRIA Multispeech Team, emmanuel.vincent@inria.fr
http://members.loria.fr/evincent/Cons: the application must meet the requirements of the French Directorate General of Armament (Direction générale de l'armement, DGA). Context
When a person has their hands busy performing a task like driving a car or piloting an airplane, voice is a fast and efficient way to achieve interaction. In aeronautical communications, the English language is most often compulsory. Unfortunately, a large part of the pilots are not native English and speak with an accent dependent on their native language and are therefore influenced by the pronunciation mechanisms of this language. Inside an aircraft cockpit, the non-native voice of the pilots and the surrounding noises are the most difficult challenges to overcome in order to have efficient automatic speech recognition (ASR). The problems of non-native speech are numerous: incorrect or approximate pronunciations, errors of agreement in gender and number, use of non-existent words, missing articles, grammatically incorrect sentences, etc. The acoustic environment adds a disturbing component to the speech signal. Much of the success of speech recognition relies on the ability to take into account different accents and ambient noises in the models used by ASR.
Automatic speech recognition has made great progress thanks to the spectacular development of deep learning. In recent years, end-to-end automatic speech recognition, which directly optimizes the probability of the output character sequence based on the input acoustic characteristics, has made great progress [Chan et al., 2016; Baevski et al., 2020; Gulati, et al., 2020].
Objectives
The recruited person will have to develop methodologies and tools to obtain high-performance non-native automatic speech recognition in the aeronautical context and more specifically in a (noisy) aircraft cockpit.
This project will be based on an end-to-end automatic speech recognition system [Shi et al., 2021] using wav2vec 2.0 [Baevski et al., 2020]. This model is one of the most efficient of the current state of the art. This wav2vec 2.0 model enables self-supervised learning of representations from raw audio data (without transcription).
How to apply: Interested candidates are encouraged to contact Irina Illina (illina@loria.fr) with the required documents (CV, transcripts, motivation letter, and recommendation letters).
Requirements & skills:
- Master's degree in speech/audio processing, computer vision, machine learning, or in a related field,
- ability to work independently as well as in a team,
- solid programming skills (Python, PyTorch), and deep learning knowledge,
- good level of written and spoken English.
References
[Baevski et al., 2020] A. Baevski, H. Zhou, A. Mohamed, and M. Auli. Wav2vec 2.0: A framework for self-supervised learning of speech representations, 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2020.
[Chan et al., 2016] W. Chan, N. Jaitly, Q. Le and O. Vinyals. Listen, attend and spell: A neural network for large vocabulary conversational speech recognition. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, pp. 4960-4964, 2016.
[Chorowski et al., 2017] J. Chorowski, N. Jaitly. Towards better decoding and language model integration in sequence to sequence models. Interspeech, 2017.
[Houlsby et al., 2019] N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, S. Gelly. Parameter-efficient transfer learning for NLP. International Conference on Machine Learning, PMLR, pp. 2790–2799, 2019.
[Gulati et al., 2020] A. Gulati, J. Qin, C.-C. Chiu, N. Parmar, Y. Zhang, J. Yu, W. Han, S. Wang, Z. Zhang, Y. Wu, and R. Pang. Conformer: Convolution-augmented transformer for speech recognition. Interspeech, 2020.
[Shi et al., 2021] X. Shi, F. Yu, Y. Lu, Y. Liang, Q. Feng, D. Wang, Y. Qian, and L. Xie. The accented english speech recognition challenge 2020: open datasets, tracks, baselines, results and methods. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6918–6922, 2021.
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| 6-11 | (2023-05-30) PhD position in NLP, @ Jozef International postgraduate School (Slovenia) and La Rochelle University (France).
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| 6-12 | (2023-06-01) PhD position @ Computer Science Lab in Bordeaux, France (LaBRI) and the LORIA (Nancy, France) In the framework of the PEPR Santé numérique “Autonom-Health” project (Health, behaviors and autonomous digital technologies), the speech and language research group at the Computer Science Lab in Bordeaux, France (LaBRI) and the LORIA (Nancy, France) are looking for candidates for a fully funded PhD position (36 months). The « Autonom-Health » project is a collaborative project on digital health between SANPSY, LaBRI, LORIA, ISIR and LIRIS. The abstract of the « Autonom-Health » project can be found at the end of this email.
The missions that will be addressed by the retained candidates are among these tasks, according to the profile of the candidate:
- Data collection tasks:
- Definition of scenarii for collecting spontaneous speech using Social Interactive Agents (SIAs) - Collection of patient/doctor interactions during clinical interviews - ASR-related tasks - Evaluate and improve the performances of our end2end ESPNET-based ASR system for French real-world spontaneous data recorded from healthy subjects and patients, - Adaptation of the ASR system to clinical interviews domain, - Automatic phonetic transcription / alignment using end2end architectures - Adapting ASR transcripts to be used with semantic analysis tools developed at LORIA - Speech analysis tasks - Analysis of vocal biomarkers for different diseases: adaptation of our biomarkers defined for sleepiness, research of new biomarkers targeted to specific diseases. The position is to be hosted at LaBRI, but depending on the profile of the candidate, close collaboration is expected either with the LORIA teams : « Multispeech » (contact: Emmanuel Vincent emmanuel.vincent@inria.fr) and/or the « Sémagramme » (contact: Maxime Amblard maxime.amblard@loria.fr). Gross salary: approx. 2044 €/month Starting date: October 2023
Required qualifications: Master in Signal processing / speech analysis / computer science Skills: Python programming, statistical learning (machine learning, deep learning), automatic signal/speech processing, excellent command of French (interactions with French patients and clinicians), good level of scientific English. Know-how: Familiarity with the ESPNET toolbox and/or deep learning frameworks, knowledge of automatic speech processing system design. Social skills: good ability to integrate into multi-disciplinary teams, ability to communicate with non-experts. Applications: To apply, please send by email at jean-luc.rouas@labri.fr a single PDF file containing a full CV, cover letter (describing your personal qualifications, research interests and motivation for applying), contact information of two referees and academic certificates (Master, Bachelor certificates). —— Abstract of the « Autonom-Health » project: Western populations face an increase of longevity which mechanically increases the number of chronic disease patients to manage. Current healthcare strategies will not allow to maintain a high level of care with a controlled cost in the future and E health can optimize the management and costs of our health care systems. Healthy behaviors contribute to prevention and optimization of chronic diseases management, but their implementation is still a major challenge. Digital technologies could help their implementation through numeric behavioral medicine programs to be developed in complement (and not substitution) to the existing care in order to focus human interventions on the most severe cases demanding medical interventions. However, to do so, we need to develop digital technologies which should be: i) Ecological (related to real-life and real-time behavior of individuals and to social/environmental constraints); ii) Preventive (from healthy subjects to patients); iii) Personalized (at initiation and adapted over the course of treatment) ; iv) Longitudinal (implemented over long periods of time) ; v) Interoperated (multiscale, multimodal and high-frequency); vi) Highly acceptable (protecting users’ privacy and generating trustability).
The above-mentioned challenges will be disentangled with the following specific goals: Goal 1: Implement large-scale diagnostic evaluations (clinical and biomarkers) and behavioral interventions (physical activities, sleep hygiene, nutrition, therapeutic education, cognitive behavioral therapies...) on healthy subjects and chronic disease patients. This will require new autonomous digital technologies (i.e. virtual Socially Interactive Agents SIAs, smartphones, wearable sensors). Goal 2: Optimize clinical phenotyping by collecting and analyzing non-intrusive data (i.e. voice, geolocalisation, body motion, smartphone footprints, ...) which will potentially complement clinical data and biomarkers data from patient cohorts. Goal 3: Better understand psychological, economical and socio-cultural factors driving acceptance and engagement with the autonomous digital technologies and the proposed numeric behavioral interventions. Goal 4: Improve interaction modalities of digital technologies to personalize and optimize long-term engagement of users. Goal 5: Organize large scale data collection, storage and interoperability with existing and new data sets (i.e, biobanks, hospital patients cohorts and epidemiological cohorts) to generate future multidimensional predictive models for diagnosis and treatment. Each goal will be addressed by expert teams through complementary work-packages developed sequentially or in parallel. A first modeling phase (based on development and experimental testings), will be performed through this project. A second phase funded via ANR calls will allow to recruit new teams for large scale testing phase. This project will rely on population-based interventions in existing numeric cohorts (i.e KANOPEE) where virtual agents interact with patients at home on a regular basis. Pilot hospital departments will also be involved for data management supervised by information and decision systems coordinating autonomous digital Cognitive Behavioral interventions based on our virtual agents. The global solution based on empathic Human-Computer Interactions will help targeting, diagnose and treat subjects suffering from dysfunctional behavioral (i.e. sleep deprivation, substance use...) but also sleep and mental disorders. The expected benefits from such a solution will be an increased adherence to treatment, a strong self-empowerment to improve autonomy and finally a reduction of long-term risks for the subjects and patients using this system. Our program should massively improve healthcare systems and allow strong technological transfer to information systems / digital health companies and the pharma industry.
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| 6-13 | (2023-06-02) Open faculty position at KU Leuven, Belgium: junior professor in Synergistic Processing of Multisensory Data for Audio-Visual Understanding Open faculty position at KU Leuven, Belgium: junior professor in Synergistic Processing
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| 6-14 | (2023-06-04) PhD in ML/NLP @ Dauphine Université PSL, Paris and Université Grenoble Alpes, France PhD in ML/NLP – Fairness and self-supervised learning for speech processing 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 This thesis is in the context of the ANR project E-SSL (Efficient Self-Supervised Learning for Inclusive and Innovative Speech Technologies). 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.
- 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
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.
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| 6-15 | (2023-06-06) Postdoc in recognition and translation @LABRI, Bordeaux, France In the framework of the European FETPROACT « Fvllmonti » project and the PEPR Santé numérique “Autonom-Health” project, the speech and language research group at the Computer Science Lab in Bordeaux, France (LaBRI) is looking for candidates for a 24-months post-doctoral position. The « Fvllmonti » project is a collaborative project on new transistor architectures applied to speech recognition and machine translation between IMS, LaBRI, LAAS, INL, EPFL, GTS and Namlab. More information on the project is available at www.fvllmonti.eu The « Autonom-Health » project is a collaborative project on digital health between SANPSY, LaBRI, LORIA, ISIR and LIRIS. The abstract of the « Autonom-Health » project can be found at the end of this email. The missions that will be addressed by the retained candidate are among these selected tasks, according to the profile of the candidate: - Data collection tasks:
- Definition of scenarii for collecting spontaneous speech using Social Interactive Agents (SIAs)
- ASR-related tasks
- Evaluate and improve the performances of our end2end ESPNET-based ASR system for French real-world spontaneous data recorded from healthy subjects and patients,
- Automatic phonetic transcription / alignment using end2end architectures
- Speech analysis tasks:
- Automatic social affect/emotion/attitudes recognition on speech samples
- Analysis of vocal biomarkers for different diseases: adaptation of our biomarkers defined for sleepiness, research of new biomarkers targeted to specific diseases.
The position is to be hosted at LaBRI, but depending on the profile of the candidate, close collaboration is expected either with the « Multispeech » (contact: Emmanuel Vincent) and/or the « Sémagramme » (contact: Maxime Amblard) teams at LORIA. Gross salary: approx. 2686 €/month Starting data: As soon as possible
Required qualifications: PhD in Signal processing / speech analysis / computer science / language sciences
Skills: Python programming, statistical learning (machine learning, deep learning), automatic signal/speech processing, good command of French (interactions with French patients and clinicians), good level of scientific English.
Know-how: Familiarity with the ESPNET toolbox and/or deep learning frameworks, knowledge of automatic speech processing system design.
Social skills: good ability to integrate into multi-disciplinary teams, ability to communicate with non-experts.
Applications: To apply, please send by email at jean-luc.rouas@labri.fr a single PDF file containing a full CV (including publication list), cover letter (describing your personal qualifications, research interests and motivation for applying), evidence for software development experience (active Github/Gitlab profile or similar), two of your key publications, contact information of two referees and academic certificates (PhD, Diploma/Master, Bachelor certificates).
However, to do so, we need to develop digital technologies which should be: i) Ecological (related to real-life and real-time behavior of individuals and to social/environmental constraints); ii) Preventive (from healthy subjects to patients); iii) Personalized (at initiation and adapted over the course of treatment) ; iv) Longitudinal (implemented over long periods of time) ; v) Interoperated (multiscale, multimodal and high-frequency); vi) Highly acceptable (protecting users’ privacy and generating trustability).
The above-mentioned challenges will be disentangled with the following specific goals: Goal 1: Implement large-scale diagnostic evaluations (clinical and biomarkers) and behavioral interventions (physical activities, sleep hygiene, nutrition, therapeutic education, cognitive behavioral therapies...) on healthy subjects and chronic disease patients. This will require new autonomous digital technologies (i.e. virtual Socially Interactive Agents SIAs, smartphones, wearable sensors). Goal 2: Optimize clinical phenotyping by collecting and analyzing non-intrusive data (i.e. voice, geolocalisation, body motion, smartphone footprints, ...) which will potentially complement clinical data and biomarkers data from patient cohorts. Goal 3: Better understand psychological, economical and socio-cultural factors driving acceptance and engagement with the autonomous digital technologies and the proposed numeric behavioral interventions. Goal 4: Improve interaction modalities of digital technologies to personalize and optimize long-term engagement of users. Goal 5: Organize large scale data collection, storage and interoperability with existing and new data sets (i.e, biobanks, hospital patients cohorts and epidemiological cohorts) to generate future multidimensional predictive models for diagnosis and treatment. Each goal will be addressed by expert teams through complementary work-packages developed sequentially or in parallel. A first modeling phase (based on development and experimental testings), will be performed through this project. A second phase funded via ANR calls will allow to recruit new teams for large scale testing phase. This project will rely on population-based interventions in existing numeric cohorts (i.e KANOPEE) where virtual agents interact with patients at home on a regular basis. Pilot hospital departments will also be involved for data management supervised by information and decision systems coordinating autonomous digital Cognitive Behavioral interventions based on our virtual agents. The global solution based on empathic Human-Computer Interactions will help targeting, diagnose and treat subjects suffering from dysfunctional behavioral (i.e. sleep deprivation, substance use...) but also sleep and mental disorders. The expected benefits from such a solution will be an increased adherence to treatment, a strong self-empowerment to improve autonomy and finally a reduction of long-term risks for the subjects and patients using this system. Our program should massively improve healthcare systems and allow strong technological transfer to information systems / digital health companies and the pharma industry.
In the framework of the European FETPROACT « Fvllmonti » project and the PEPR Santé numérique “Autonom-Health” project, the speech and language research group at the Computer Science Lab in Bordeaux, France (LaBRI) is looking for candidates for a 24-months post-doctoral position. The « Fvllmonti » project is a collaborative project on new transistor architectures applied to speech recognition and machine translation between IMS, LaBRI, LAAS, INL, EPFL, GTS and Namlab. More information on the project is available at www.fvllmonti.eu The « Autonom-Health » project is a collaborative project on digital health between SANPSY, LaBRI, LORIA, ISIR and LIRIS. The abstract of the « Autonom-Health » project can be found at the end of this email. The missions that will be addressed by the retained candidate are among these selected tasks, according to the profile of the candidate: - Data collection tasks:
- Definition of scenarii for collecting spontaneous speech using Social Interactive Agents (SIAs)
- ASR-related tasks
- Evaluate and improve the performances of our end2end ESPNET-based ASR system for French real-world spontaneous data recorded from healthy subjects and patients,
- Automatic phonetic transcription / alignment using end2end architectures
- Speech analysis tasks:
- Automatic social affect/emotion/attitudes recognition on speech samples
- Analysis of vocal biomarkers for different diseases: adaptation of our biomarkers defined for sleepiness, research of new biomarkers targeted to specific diseases.
The position is to be hosted at LaBRI, but depending on the profile of the candidate, close collaboration is expected either with the « Multispeech » (contact: Emmanuel Vincent) and/or the « Sémagramme » (contact: Maxime Amblard) teams at LORIA. Gross salary: approx. 2686 €/month Starting data: As soon as possible
Required qualifications: PhD in Signal processing / speech analysis / computer science / language sciences
Skills: Python programming, statistical learning (machine learning, deep learning), automatic signal/speech processing, good command of French (interactions with French patients and clinicians), good level of scientific English.
Know-how: Familiarity with the ESPNET toolbox and/or deep learning frameworks, knowledge of automatic speech processing system design.
Social skills: good ability to integrate into multi-disciplinary teams, ability to communicate with non-experts.
Applications: To apply, please send by email at jean-luc.rouas@labri.fr a single PDF file containing a full CV (including publication list), cover letter (describing your personal qualifications, research interests and motivation for applying), evidence for software development experience (active Github/Gitlab profile or similar), two of your key publications, contact information of two referees and academic certificates (PhD, Diploma/Master, Bachelor certificates).
However, to do so, we need to develop digital technologies which should be: i) Ecological (related to real-life and real-time behavior of individuals and to social/environmental constraints); ii) Preventive (from healthy subjects to patients); iii) Personalized (at initiation and adapted over the course of treatment) ; iv) Longitudinal (implemented over long periods of time) ; v) Interoperated (multiscale, multimodal and high-frequency); vi) Highly acceptable (protecting users’ privacy and generating trustability).
The above-mentioned challenges will be disentangled with the following specific goals: Goal 1: Implement large-scale diagnostic evaluations (clinical and biomarkers) and behavioral interventions (physical activities, sleep hygiene, nutrition, therapeutic education, cognitive behavioral therapies...) on healthy subjects and chronic disease patients. This will require new autonomous digital technologies (i.e. virtual Socially Interactive Agents SIAs, smartphones, wearable sensors). Goal 2: Optimize clinical phenotyping by collecting and analyzing non-intrusive data (i.e. voice, geolocalisation, body motion, smartphone footprints, ...) which will potentially complement clinical data and biomarkers data from patient cohorts. Goal 3: Better understand psychological, economical and socio-cultural factors driving acceptance and engagement with the autonomous digital technologies and the proposed numeric behavioral interventions. Goal 4: Improve interaction modalities of digital technologies to personalize and optimize long-term engagement of users. Goal 5: Organize large scale data collection, storage and interoperability with existing and new data sets (i.e, biobanks, hospital patients cohorts and epidemiological cohorts) to generate future multidimensional predictive models for diagnosis and treatment. Each goal will be addressed by expert teams through complementary work-packages developed sequentially or in parallel. A first modeling phase (based on development and experimental testings), will be performed through this project. A second phase funded via ANR calls will allow to recruit new teams for large scale testing phase. This project will rely on population-based interventions in existing numeric cohorts (i.e KANOPEE) where virtual agents interact with patients at home on a regular basis. Pilot hospital departments will also be involved for data management supervised by information and decision systems coordinating autonomous digital Cognitive Behavioral interventions based on our virtual agents. The global solution based on empathic Human-Computer Interactions will help targeting, diagnose and treat subjects suffering from dysfunctional behavioral (i.e. sleep deprivation, substance use...) but also sleep and mental disorders. The expected benefits from such a solution will be an increased adherence to treatment, a strong self-empowerment to improve autonomy and finally a reduction of long-term risks for the subjects and patients using this system. Our program should massively improve healthcare systems and allow strong technological transfer to information systems / digital health companies and the pharma industry.
In the framework of the European FETPROACT « Fvllmonti » project and the PEPR Santé numérique “Autonom-Health” project, the speech and language research group at the Computer Science Lab in Bordeaux, France (LaBRI) is looking for candidates for a 24-months post-doctoral position. The « Fvllmonti » project is a collaborative project on new transistor architectures applied to speech recognition and machine translation between IMS, LaBRI, LAAS, INL, EPFL, GTS and Namlab. More information on the project is available at www.fvllmonti.eu The « Autonom-Health » project is a collaborative project on digital health between SANPSY, LaBRI, LORIA, ISIR and LIRIS. The abstract of the « Autonom-Health » project can be found at the end of this email. The missions that will be addressed by the retained candidate are among these selected tasks, according to the profile of the candidate: - Data collection tasks:
- Definition of scenarii for collecting spontaneous speech using Social Interactive Agents (SIAs)
- ASR-related tasks
- Evaluate and improve the performances of our end2end ESPNET-based ASR system for French real-world spontaneous data recorded from healthy subjects and patients,
- Automatic phonetic transcription / alignment using end2end architectures
- Speech analysis tasks:
- Automatic social affect/emotion/attitudes recognition on speech samples
- Analysis of vocal biomarkers for different diseases: adaptation of our biomarkers defined for sleepiness, research of new biomarkers targeted to specific diseases.
The position is to be hosted at LaBRI, but depending on the profile of the candidate, close collaboration is expected either with the « Multispeech » (contact: Emmanuel Vincent) and/or the « Sémagramme » (contact: Maxime Amblard) teams at LORIA. Gross salary: approx. 2686 €/month Starting data: As soon as possible
Required qualifications: PhD in Signal processing / speech analysis / computer science / language sciences
Skills: Python programming, statistical learning (machine learning, deep learning), automatic signal/speech processing, good command of French (interactions with French patients and clinicians), good level of scientific English.
Know-how: Familiarity with the ESPNET toolbox and/or deep learning frameworks, knowledge of automatic speech processing system design.
Social skills: good ability to integrate into multi-disciplinary teams, ability to communicate with non-experts.
Applications: To apply, please send by email at jean-luc.rouas@labri.fr a single PDF file containing a full CV (including publication list), cover letter (describing your personal qualifications, research interests and motivation for applying), evidence for software development experience (active Github/Gitlab profile or similar), two of your key publications, contact information of two referees and academic certificates (PhD, Diploma/Master, Bachelor certificates).
However, to do so, we need to develop digital technologies which should be: i) Ecological (related to real-life and real-time behavior of individuals and to social/environmental constraints); ii) Preventive (from healthy subjects to patients); iii) Personalized (at initiation and adapted over the course of treatment) ; iv) Longitudinal (implemented over long periods of time) ; v) Interoperated (multiscale, multimodal and high-frequency); vi) Highly acceptable (protecting users’ privacy and generating trustability).
The above-mentioned challenges will be disentangled with the following specific goals: Goal 1: Implement large-scale diagnostic evaluations (clinical and biomarkers) and behavioral interventions (physical activities, sleep hygiene, nutrition, therapeutic education, cognitive behavioral therapies...) on healthy subjects and chronic disease patients. This will require new autonomous digital technologies (i.e. virtual Socially Interactive Agents SIAs, smartphones, wearable sensors). Goal 2: Optimize clinical phenotyping by collecting and analyzing non-intrusive data (i.e. voice, geolocalisation, body motion, smartphone footprints, ...) which will potentially complement clinical data and biomarkers data from patient cohorts. Goal 3: Better understand psychological, economical and socio-cultural factors driving acceptance and engagement with the autonomous digital technologies and the proposed numeric behavioral interventions. Goal 4: Improve interaction modalities of digital technologies to personalize and optimize long-term engagement of users. Goal 5: Organize large scale data collection, storage and interoperability with existing and new data sets (i.e, biobanks, hospital patients cohorts and epidemiological cohorts) to generate future multidimensional predictive models for diagnosis and treatment. Each goal will be addressed by expert teams through complementary work-packages developed sequentially or in parallel. A first modeling phase (based on development and experimental testings), will be performed through this project. A second phase funded via ANR calls will allow to recruit new teams for large scale testing phase. This project will rely on population-based interventions in existing numeric cohorts (i.e KANOPEE) where virtual agents interact with patients at home on a regular basis. Pilot hospital departments will also be involved for data management supervised by information and decision systems coordinating autonomous digital Cognitive Behavioral interventions based on our virtual agents. The global solution based on empathic Human-Computer Interactions will help targeting, diagnose and treat subjects suffering from dysfunctional behavioral (i.e. sleep deprivation, substance use...) but also sleep and mental disorders. The expected benefits from such a solution will be an increased adherence to treatment, a strong self-empowerment to improve autonomy and finally a reduction of long-term risks for the subjects and patients using this system. Our program should massively improve healthcare systems and allow strong technological transfer to information systems / digital health companies and the pharma industry.
In the framework of the European FETPROACT « Fvllmonti » project and the PEPR Santé numérique “Autonom-Health” project, the speech and language research group at the Computer Science Lab in Bordeaux, France (LaBRI) is looking for candidates for a 24-months post-doctoral position. The « Fvllmonti » project is a collaborative project on new transistor architectures applied to speech recognition and machine translation between IMS, LaBRI, LAAS, INL, EPFL, GTS and Namlab. More information on the project is available at www.fvllmonti.eu The « Autonom-Health » project is a collaborative project on digital health between SANPSY, LaBRI, LORIA, ISIR and LIRIS. The abstract of the « Autonom-Health » project can be found at the end of this email. The missions that will be addressed by the retained candidate are among these selected tasks, according to the profile of the candidate: - Data collection tasks:
- Definition of scenarii for collecting spontaneous speech using Social Interactive Agents (SIAs)
- ASR-related tasks
- Evaluate and improve the performances of our end2end ESPNET-based ASR system for French real-world spontaneous data recorded from healthy subjects and patients,
- Automatic phonetic transcription / alignment using end2end architectures
- Speech analysis tasks:
- Automatic social affect/emotion/attitudes recognition on speech samples
- Analysis of vocal biomarkers for different diseases: adaptation of our biomarkers defined for sleepiness, research of new biomarkers targeted to specific diseases.
The position is to be hosted at LaBRI, but depending on the profile of the candidate, close collaboration is expected either with the « Multispeech » (contact: Emmanuel Vincent) and/or the « Sémagramme » (contact: Maxime Amblard) teams at LORIA. Gross salary: approx. 2686 €/month Starting data: As soon as possible
Required qualifications: PhD in Signal processing / speech analysis / computer science / language sciences
Skills: Python programming, statistical learning (machine learning, deep learning), automatic signal/speech processing, good command of French (interactions with French patients and clinicians), good level of scientific English.
Know-how: Familiarity with the ESPNET toolbox and/or deep learning frameworks, knowledge of automatic speech processing system design.
Social skills: good ability to integrate into multi-disciplinary teams, ability to communicate with non-experts.
Applications: To apply, please send by email at jean-luc.rouas@labri.fr a single PDF file containing a full CV (including publication list), cover letter (describing your personal qualifications, research interests and motivation for applying), evidence for software development experience (active Github/Gitlab profile or similar), two of your key publications, contact information of two referees and academic certificates (PhD, Diploma/Master, Bachelor certificates).
However, to do so, we need to develop digital technologies which should be: i) Ecological (related to real-life and real-time behavior of individuals and to social/environmental constraints); ii) Preventive (from healthy subjects to patients); iii) Personalized (at initiation and adapted over the course of treatment) ; iv) Longitudinal (implemented over long periods of time) ; v) Interoperated (multiscale, multimodal and high-frequency); vi) Highly acceptable (protecting users’ privacy and generating trustability).
The above-mentioned challenges will be disentangled with the following specific goals: Goal 1: Implement large-scale diagnostic evaluations (clinical and biomarkers) and behavioral interventions (physical activities, sleep hygiene, nutrition, therapeutic education, cognitive behavioral therapies...) on healthy subjects and chronic disease patients. This will require new autonomous digital technologies (i.e. virtual Socially Interactive Agents SIAs, smartphones, wearable sensors). Goal 2: Optimize clinical phenotyping by collecting and analyzing non-intrusive data (i.e. voice, geolocalisation, body motion, smartphone footprints, ...) which will potentially complement clinical data and biomarkers data from patient cohorts. Goal 3: Better understand psychological, economical and socio-cultural factors driving acceptance and engagement with the autonomous digital technologies and the proposed numeric behavioral interventions. Goal 4: Improve interaction modalities of digital technologies to personalize and optimize long-term engagement of users. Goal 5: Organize large scale data collection, storage and interoperability with existing and new data sets (i.e, biobanks, hospital patients cohorts and epidemiological cohorts) to generate future multidimensional predictive models for diagnosis and treatment. Each goal will be addressed by expert teams through complementary work-packages developed sequentially or in parallel. A first modeling phase (based on development and experimental testings), will be performed through this project. A second phase funded via ANR calls will allow to recruit new teams for large scale testing phase. This project will rely on population-based interventions in existing numeric cohorts (i.e KANOPEE) where virtual agents interact with patients at home on a regular basis. Pilot hospital departments will also be involved for data management supervised by information and decision systems coordinating autonomous digital Cognitive Behavioral interventions based on our virtual agents. The global solution based on empathic Human-Computer Interactions will help targeting, diagnose and treat subjects suffering from dysfunctional behavioral (i.e. sleep deprivation, substance use...) but also sleep and mental disorders. The expected benefits from such a solution will be an increased adherence to treatment, a strong self-empowerment to improve autonomy and finally a reduction of long-term risks for the subjects and patients using this system. Our program should massively improve healthcare systems and allow strong technological transfer to information systems / digital health companies and the pharma industry.
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| 6-16 | (2023-06-02) Transcriptors for ELDA Paris France ELDA (Evaluations and Language resources Disctribution Agency) looks for full/part time transcriptors for transcription of phone calls in the financial domain. Location: ELDA (Paris-France) Latest starting date: July 2023 Languages and mission details
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| 6-17 | (2023-06-08) Postdoc @ ENS,Paris, France DRhyaDS A new framework for understanding the Dynamic Rhythms and Decoding of Speech Job Title - Postdoctoral Researcher Disciplines and Areas of Research - Speech science, Psycholinguistics, Psychoacoustics Contract Duration - 1 Year Research Overview: The DRhyaDS project aims to develop a new framework for understanding the dynamic rhythms and decoding of speech. It focuses on exploring the temporal properties of speech and their contribution to speech perception. The project challenges the conventional view that speech rhythm perception relies on a one-to-one association between specific modulation frequencies in the speech signal and linguistic units. One of the key objectives of the project is to investigate the impact of language-specific temporal characteristics on speech dynamics. The project team will analyze two corpora of semi-spontaneous speech data from French and German, representing syllable-timed and stress-timed languages, respectively. Various acoustic analyses will be conducted on these speech corpora to explore the variability of slow temporal modulations in speech at an individual level. This comprehensive acoustic exploration will involve extracting and analyzing prosody, spectral properties, temporal dynamics, and rhythmic patterns. By examining these acoustic parameters, the project aims to uncover intricate details about the structure and variation of speech signals across languages and speakers, contributing to a more nuanced understanding of the dynamic nature of spoken language and its role in human communication. Environment: The selected candidate will be an integral part of an international research team and will work in a collaborative and stimulating lab environment. The project brings together a FrancoGerman team of experts in linguistics, psychoacoustics and cognitive neuroscience, led by Dr. Léo Varnet (CNRS, ENS Paris) and Dr. Alessandro Tavano (Max Planck Institute, Goethe University Frankfurt). The successful candidate will work under the supervision of Dr Léo Varnet, at the Laboratoire des Systèmes Perceptifs (ENS Paris). Job description: This is a one-year postdoctoral contract position, offering a net salary in accordance with French legislation (~2500€/month + social and medical benefits). Women and minorities are strongly encouraged to apply. The successful candidate will participate in research activities, collaborate with team members, and contribute to scientific publications and communications. Additionally, they will have the autonomy to suggest and implement their own analysis techniques and approaches. Their responsibilities will include: - Taking a lead role in collecting a comprehensive corpus of French speech data, adhering to a rigorous data collection protocol - Collaborating closely with the German team to leverage the existing German speech corpus for comparative analysis and cross-linguistic investigations - Conducting in-depth acoustic analysis of the corpora, employing advanced techniques to investigate the variability and dynamics of slow temporal modulations in speech - Actively participating in team meetings, workshops, and conferences to present research progress, exchange ideas, and contribute to the intellectual growth of the project - Engaging in science outreach activities to promote the project's research outcomes and facilitate public understanding of speech perception and language processing. Qualifications: - A recently obtained PhD in a relevant field (e.g., linguistics, psychology, neuroscience, computational sciences) - Strong expertise in linguistics, speech perception, acoustic analysis, and statistical methods - Proficiency in programming languages commonly used in speech research. Knowledge of MATLAB would be particularly valuable for data processing and analysis within the project. - Strong written and verbal communication skills in English. Candidates with proficiency in French and/or German language skills would be particularly appreciated, as it would enable a deeper understanding of the linguistic characteristics of the respective corpora. Application process To apply for this position, please submit a CV and a cover letter (in French or English) along with the names and contact information of 2 referees to Léo Varnet (leo.varnet@cnrs.fr). The application deadline is 31th July 2023. Interviews will be conducted in September. The ideal start date is October-November 2023, with some flexibility allowed. Feel free to get in touch informally to discuss this position
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| 6-18 | (2023-06-16) PhD funded position@ INRIA France Inria is opening a fully funded PhD position on multimodal speech
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| 6-19 | (2023-06-16) Post doc @ IMAG, Grenoble, France Call for postdoc applications in Natural Language Processing for the automatic detection of gender stereotypes in the French media (Grenoble Alps University, France) Starting date: flexible, November 30, 2023, at the latest Duration: full-time position for 12 months Salary: according to experience (up to 4142€/ month) Application Deadline: Open until filled Location: The position will be based in Grenoble, France. This is not a remote work. Keywords: natural language processing, gender stereotypes bias, corpus analysis, language models, transfer learning, deep learning *Context* The University of Grenoble Alps (UGA) has an open position for a highly motivated postdoc researcher to joint the multidisciplinary GenderedNews project. Natural Language Processing models trained on large amount of on-line content, have quickly opened new perspectives to process on-line large amount of on-line content for measuring gender bias in a daily basis (see our project https://gendered-news.imag.fr/ ). Regarding research on stereotypes, most recent works have studied Language Models (LM) from a stereotype perspective by providing specific corpora such as StereoSet (Nadeem et al., 2020) or CrowS-Pairs (Nangia et al. 2020). However, these studies are focusing on the quantifying of bias in the LM predictions rather than bias in the original data (Choenni et al., 2021). Furthermore, most of these studies ignore named entities (Deshpande et al., 2022) which account for an important part of the referents and speakers in news. In this project, we intend to build corpora, methods and NLP tools to qualify the differences between the language used to describe groups of people in French news. *Main Tasks* The successful postdoc will be responsible for day-to-day running of the research project, under the supervision of François Portet (Prof UGA at LIG) and Gilles Bastin (prof UGA at PACTE). Regular meetings will take place every two weeks. - Defining the dimensions of stereotypes to be investigated and the possible metrics that can be processed from a machine learning perspective. - Exploring, managing and curating news corpora in French for stereotypes investigation, with a view to making them widely available to the community to favor reproducible research and comparison. - Studying and developing new computational models to process large number of texts to reveal stereotype bias in news. Make use of pretrained models for the task. - Evaluate the methods on curated focused corpus and apply it to the unseen real longitudinal corpus and analyze the results with the team. - Preparing articles for submission to peer-reviewed conferences and journals. - Organizing progress meetings and liaising between members of the team. The hired person will interact with PhD students, interns and researchers being part of the GenderedNews project. According to his/her background his/her own interests and in accordance with the project's objective, the hired person will have the possibility to orient the research in different directions. *Scientific Environment* The recruited person will be hosted within the GETALP teams of the LIG laboratory (https://lig-getalp.imag.fr/), which offers a dynamic, international, and stimulating environment for conducting high-level multidisciplinary research. The person will have access to large datasets of French news, GPU servers, to support for missions as well as to the scientific activities of the labs. The team is housed in a modern building (IMAG) located in a 175-hectare landscaped campus that was ranked as the eighth most beautiful campus in Europe by the Times Higher Education magazine in 2018. The person will also closely work with Gilles Bastin (PACTE, a Sociology lab in Grenoble) and Ange Richard (PhD at LIG and PACTE). The project also includes an informal collaboration with 'Prenons la une' (https://prenonslaune.fr/) a journalists’ association which promotes a fair representation of women in the media. *Requirements* The candidate must have a PhD degree in Natural Language Processing or computer science or in the process of acquiring it. The successful candidate should have - Good knowledge of Natural Language Processing - Experience in corpus collection/formatting and manipulation. - Good programming skills in Python - Publication record in a close field of research - Willing to work in multidisciplinary and international teams - Good communication skills - Good mastering of French is required *Instructions for applying* Applications will be considered on the fly and must be addressed to François Portet (Francois.Portet@imag.fr). It is therefore advisable to apply as soon as possible. The application file should contain - Curriculum vitae - References for potential letter(s) of recommendation - One-page summary of research background and interests for the position - Publications demonstrating expertise in the aforementioned areas - Pre-defense reports and defense minutes; or summary of the thesis with the date of defense for those currently in doctoral studies *References* Deshpande et al. (2022). StereoKG: Data-Driven Knowledge Graph Construction for Cultural Knowledge and Stereotypes. arXiv preprint arXiv:2205.14036. Choenni et al. (2021). Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you? arXiv preprint arXiv:2109.10052. Nadeem et al. (2020) StereoSet: Measuring stereotypical bias in pretrained language models. ArXiv. Nangia et al. (2020) CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models. In EMNLP2020.
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| 6-20 | (2023-06-16) 6 Post-doc positions in 'Education and AI in the 21st century. Technology-enabled innovations in subject-specific teaching settings (PostdocTEIFUN)' , Tübingen and Stuttgart, Germany
the new Postdoc-Kolleg 'Education and AI in the 21st century. Technology-enabled innovations in subject-specific teaching settings (PostdocTEIFUN)' of the Tübingen School of Education (TüSE) and Professional School of Education Stuttgart-Ludwigsburg (PSE) will start in 2024.
It is offering six Post-doc positions funded for six years (100% TVL E14) to conduct interdisciplinary research in the field of education and AI.
The official announcement can be found here:
Among the various potential research fields, the following could be of interest here:
'Development of didactically informed Intelligent Language Tutoring approaches targeting spoken language learning for the English classroom. Potential directions include but are not limited to the realization of perceptual training, input enhancement, automatic corrective feedback, or prosodic complexity analysis.'
Interested? Please contact us at: Sabine Zerbian, sabine.zerbian@ifla.uni-stuttgart.de Detmar Meurers, detmar.meurers@uni-tuebingen.de
Note that there is a tight deadline for applications: 30.06.2023
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| 6-21 | (2023-06-17) PhD @ NTNU, Trondheim, Norway The announcement is here:
Deadline is 2023-07-31.
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| 6-22 | (2023-06-20) PhD position @ University of Applied Sciences, Hochschule Hannover, Germany
In a joint research project *VidQA*, our collaboration partner from University of Applied Sciences (Prof. Christian Wartena, HsH Hochschule Hannover) offers a full position (three years, as soon as possible) for a PhD student:
*VidQA* is a joint research project of the Institute for Applied Data Science Hannover (DATA|H) of the HsH, the Research Center L3S of the Leibniz University and the TIB – Leibniz Information Centre for Science and Technology. The goal of the project is the development and evaluation of new methods for semi-automatic generation of questions and answers for learning videos. Here, we pursue several research questions, such as, among others, the aspect of multimodality of video-based learning media, the generation of distractors ('wrong answers') for multiple-choice questions, and the automatic evaluation of answers for open-ended question formats.
*What you can expect*: - Develop and implement procedures for generating (multiple choice) questions, answers, and distractors. - Development and implementation of procedures for scoring free text answers - Collaboration in the development and evaluation of a system for examing comprehension of learning videos - Publication of research and development results at conferences and in professional journals - Participation in the organization the project
*Requirements* Research Field: Computer science Education Level: Master Degree or equivalent Skills/Qualifications - Master's degree (or equivalent) in computer science or computational linguistics - In-depth knowledge in the field of artificial intelligence and machine learning - Proven knowledge in Natural Language Processing (NLP) - Gender and diversity skills
Languages: ENGLISH Level: Excellent
Languages: GERMAN Level: Good
Website for additional job details: https://karriere.hs-hannover.de/bewerbung/beschreibung-900000104-10057.html
Where to apply: https://karriere.hs-hannover.de/bewerbung/beschreibung-900000104-10057.html *First Contact* ************************************ Prof. Dr. Christian Wartena University of Applied Sciences (HsH Hochschule Hannover) Institute for Applied Data Science Hannover (DATA|H) E-Mail: christian.wartena@hs-hannover.de
Postal address City: Hannover Website: https://www.hs-hannover.de/forschung/forschungsaktivitaeten/forschungscluster/smart-data-analytics Street: Expo Plaza 12 Postal Code: 30539
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| 6-23 | (2023-06-26) These CIFRE pleinement financée, IMAG et Eloquant, Grenoble, France
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| 6-24 | (2023-06-27) Research Associate in Integrated Multitask Neural Speech Labelling, University of Sheffield, UK Deadline July 13th, 2023
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| 6-25 | (2023-07-01) Offre de thèse en 'Apprentissage profond pour l'identification du locuteur et séparation de la parole', CNRS Pour plus d'informations voir :
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| 6-26 | (2023-07-19) Ingénieur chef de projet ressources et technologies linguistiques, INRIA, Nancy,France Ingénieur chef de projet ressources et technologies linguistiques Ville : Nancy, France
Date de prise de fonction souhaitée : 2023-10-01 Type de contrat : CDD 4 ans Niveau de diplôme exigé : BAC+5 ou équivalent Niveau d’expérience souhaité : de 3 à 5 ans Pour postuler : https://jobs.inria.fr/public/classic/fr/offres/2023-06574 Pour plus d’informations, contacter : Slim.Ouni@loria.fr Description complète du poste : https://jobs.inria.fr/public/classic/fr/offres/2023-06574 Poste : Ingénieur chef de projet ressources et technologies linguistiques CONTEXTE Ce poste se place dans le cadre du Défi Inria COLaF (Corpus et Outils pour les Langues de France), qui est une collaboration entre les équipes ALMAnaCH et MULTISPEECH. L’objectif du Défi est de développer et mettre à disposition des technologies numériques linguistiques pour la francophonie et les langues de France, en contribuant à la création de corpus de données inclusifs, de modèles, et de briques logicielles. L’équipe ALMAnaCH focalise sur le texte et l’équipe MULTISPEECH sur la parole multimodale. Les deux principaux objectifs de ce projet sont : (1) La collecte de corpus de données francophones, massifs et inclusifs : Il s’agit de constituer de très grands corpus textuels et de parole, avec des métadonnées riches pour améliorer la robustesse des modèles face à la variation linguistique, avec une place particulière pour la variation géographico-dialectale dans le contexte de la francophonie, dont une partie pourra être multimodale (audio, image, vidéo), voire spécifique à la langue des signes française (LSF). Les données liées à la parole multimodale concerneront entre autres les dialectes, les accents, la parole des personnes âgées, des enfants et des adolescents, la LSF et les autres langues largement parlées en France. La collecte de corpus sera basée prioritairement sur les données existantes. Ces données (parole multimodale) peuvent provenir des archives de l’INA et des radio-télévisions régionales ou étrangères, mais rarement sous une forme directement exploitable, ou bien auprès des spécialistes, mais sous forme de petits corpus dispersés. La difficulté consiste d’une part à identifier et pré-traiter les données pertinentes afin d’obtenir des corpus homogènes, et d’autre part à clarifier (et si possible assouplir) les contraintes légales et les contreparties financières régissant leur usage afin d’assurer l’impact le plus large possible. Lorsque les contraintes légales ne permettent pas d’utiliser les données existantes, un effort supplémentaire de collecte de données sera nécessaire. Ce sera probablement le cas des enfants (applications à l’éducation) et les personnes âgées (applications à la santé). Selon la situation, cet effort sera sous-traité à des linguistes de terrain ou mènera à une campagne à grande échelle. Cela sera conduit en collaboration avec Le VoiceLab et la DGLFLF. (2) Le développement et la mise à disposition de technologies linguistiques inclusives : Les technologies linguistiques considérées dans ce projet par l’équipe MULTISPEECH sont la reconnaissance et la synthèse de la parole, et la génération de la langue des signes. De nombreuses technologies sont déjà commercialisées. Il s’agit donc de ne pas réinventer ces outils, mais leur apporter les modifications nécessaires, afin qu’ils puissent exploiter les corpus inclusifs créés. Les technologies qui seront utilisées dans le cadre de ce projet portent sur, y compris, mais sans s’y limiter, les tâches suivantes : • Identification et prétraitement (semi-)automatique des données pertinentes au sein de masses de données existantes. Cela inclut la détection et le remplacement d’entités nommées à des fins d’anonymisation.
• Architectures neuronales et approches adaptées aux scénarios à faibles ressources (augmentation de données, apprentissage par transfert, apprentissage faiblement/non supervisé, apprentissage actif, et combinaison entre ces diverses formes d’apprentissage)
MISSIONS L’ingénieur chef de projet aura deux missions principales : • La gestion du projet et la coordination pratique de la contribution de l’équipe MULTISPEECH au Défi Inria. L’ingénieur chef de projet travaillera en étroite collaboration avec un ingénieur « junior », un chercheur et deux doctorants, tous travaillant dans le cadre de ce projet. Il assurera un encadrement rapproché de l’ingénieur « junior » et une interaction très fréquente avec le chercheur et les doctorants. Il sera en contact également avec les membres de l’équipe MULTISPEECH. Il y aura certainement une concertation et une collaboration solide avec son homologue au sein de l’équipe ALMAnaCH.
• La collecte de données et création de corpus de parole multimodale (cela comprend : certains dialectes, les accents, les personnes âgées, les enfants et adolescents, la LSF et certaines langues largement parlées en France autre que le français). Une grande partie de la collecte des données se fera auprès d’associations de locuteurs, des producteurs de contenus et tout partenaire pertinent pour la récupération de données. L’ingénieur chef de projet sera amené à discuter, notamment les aspects juridiques, avec nos interlocuteurs.
ACTIVITÉS PRINCIPALES• Définition des différents types de corpus à collecter (identifier les corpus potentiellement exploitables, établir une priorité et un planning de collecte)
• Collecte de corpus de parole auprès de producteurs de contenus ou de tout autre partenaire. (s'assurer que les données respectent les normes et les standards de qualité)
• Négociation des contrats d'utilisation des données, en veillant à respecter les aspects juridiques (négocier les conditions d'utilisation des données avec les producteurs de contenus ou les partenaires, en veillant à ce que les droits de propriété intellectuelle soient respectés et que les aspects juridiques soient pris en compte).
• Création et mise à disposition des technologies linguistiques pour le traitement de ces corpus : une fois collectées, les données doivent être analysées et traitées de manière à en extraire des informations utiles. L’ingénieur chef de projet doit proposer des technologies et des outils parmi l’existant, nécessaires à cette analyse, et s'assurer qu'ils sont accessibles aux utilisateurs.
• Encadrement rapproché de l’ingénieur junior : accompagnement et conseil au niveau des choix techniques et stratégiques de développement.
• Concertation et animation des échanges entre les membres du projet : (1) avec le chercheur et les deux doctorants (réflexions et échanges sur les données, et leurs adéquations au Défi.) ; (2) coordination avec les membres du projet au sein de l’équipe ALMAnaCH.
• Veille technologique, en particulier dans le domaine du ce défi.
• Rédaction et présentation de documentation technique
Note : Il s’agît ici d’une liste indicative d’activités qui pourra être adaptée dans le respect de la mission telle que libellée plus haut.COMPÉTENCES PROFIL RECHERCHÉ : • Diplômé en informatique, linguistique ou toute autre formation relevant du domaine du traitement automatique de la parole ou des langues
• Expérience confirmée en gestion de projet et en communication
• Connaissance approfondie des technologies linguistiques
• Capacité à travailler en équipe et à respecter les délais
• Bonne connaissance de l'anglais
SAVOIRS • Capacité à rédiger, à publier et à présenter en français et en anglais
• Maitrise des techniques de conduite des projets et de négociation
• Bases juridiques (données personnelles, propriété intellectuelle, droit des affaires)
SAVOIR-FAIRE • Capacités d'analyse, rédactionnelles et de synthèse
• Savoir accompagner et conseiller
• Savoir développer un réseau relationnel
• Savoir mener de front différents projets en même temps
• Capacités de négociation
• Sens des responsabilités et autonomie
• Sens du contact et goût pour le travail en équipe
• Rigueur, sens des priorités et du reporting
• Qualités relationnelles (écoute- diplomatie- pouvoir de conviction)
• Appétence pour la négociation (Le VoiceLab, DGLFLF, etc.)
• Capacité d’anticipation
• Esprit d’initiative et curiosité d’esprit
Poste à temps complet, à pourvoir dès que possible. Rémunération selon l’expérience. Les candidatures doivent être déposées en ligne sur le site Inria. Le traitement des candidatures adressées par d'autres canaux n'est pas garanti. AVANTAGES • 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)
• 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
2765€ brut/mois (selon l’expérience)
À PROPOS D'INRIA Inria est l’institut national de recherche en sciences et technologies du numérique. La recherche de rang mondial, l’innovation technologique et le risque entrepreneurial constituent son ADN. Au sein de 200 équipes-projets, pour la plupart communes avec les grandes universités de recherche, plus de 3 500 chercheurs et ingénieurs y explorent des voies nouvelles, souvent dans l’interdisciplinarité et en collaboration avec des partenaires industriels pour répondre à des défis ambitieux. Inria soutient la diversité des voies de l’innovation : de l’édition open source de logiciels à la création de startups technologiques (Deeptech). À PROPOS DU CENTRE INRIA NANCY – GRAND EST Le centre Inria Nancy – Grand-Est est un des huit centres d’Inria regroupant 400 personnes, réparties dans 22 équipes de recherche, et 8 services d’appui à la recherche. Toutes ces équipes de recherche sont communes avec des partenaires académiques, et trois d’entre elles sont basées à Strasbourg. Ce centre de recherche est un acteur majeur et reconnu dans le domaine des sciences numériques. Il est au cœur d'un riche écosystème de R&D et d’innovation : PME fortement innovantes, grands groupes, start-up, incubateurs & accélérateurs, pôles de compétitivité, acteurs de la recherche et de l’enseignement supérieur, instituts de recherche technologique. ENVIRONNEMENT DE TRAVAIL L’ingénieur chef de projet travaillera au sein de l’équipe projet MULTISPEECH au Centre de recherche Inria Nancy. Les recherches de MULTISPEECH sont centrées sur la parole multimodale, notamment sur son analyse et sa génération dans le contexte de l'interaction homme-machine. Un point central de ces travaux est la conception de modèles et de techniques d'apprentissage automatique pour extraire des informations sur le contenu linguistique, l'identité et les états du locuteur, et l'environnement de la parole, et pour synthétiser la parole multimodale en utilisant des quantités limitées de données étiquetées. Pour postuler :
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| 6-27 | (2023-09-29) Offre de thèse à l'IRCAM, Paris, France Offre de thèse sur la conversion neuronale de la parole financée dans le cadre du projet ANR EVA “Explicit Voice Attributes'
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| 6-28 | (2023-08-10) Postdocs in Natural Language Processing for the automatic detection of gender, Alps University, Grenoble, France) Call for postdoc applications in Natural Language Processing for the automatic detection of gender stereotypes in the French media (Grenoble Alps University, France) Starting date: flexible, November 30, 2023, at the latest Duration: full-time position for 12 months Salary: according to experience (up to 4142€/ month) Application Deadline: Open until filled Location: The position will be based in Grenoble, France. This is not a remote work. Keywords: natural language processing, gender stereotypes bias, corpus analysis, language models, transfer learning, deep learning *Context* The University of Grenoble Alps (UGA) has an open position for a highly motivated postdoc researcher to joint the multidisciplinary GenderedNews project. Natural Language Processing models trained on large amount of on-line content, have quickly opened new perspectives to process on-line large amount of on-line content for measuring gender bias in a daily basis (see our project https://gendered-news.imag.fr/ ). Regarding research on stereotypes, most recent works have studied Language Models (LM) from a stereotype perspective by providing specific corpora such as StereoSet (Nadeem et al., 2020) or CrowS-Pairs (Nangia et al. 2020). However, these studies are focusing on the quantifying of bias in the LM predictions rather than bias in the original data (Choenni et al., 2021). Furthermore, most of these studies ignore named entities (Deshpande et al., 2022) which account for an important part of the referents and speakers in news. In this project, we intend to build corpora, methods and NLP tools to qualify the differences between the language used to describe groups of people in French news. *Main Tasks* The successful postdoc will be responsible for day-to-day running of the research project, under the supervision of François Portet (Prof UGA at LIG) and Gilles Bastin (prof UGA at PACTE). Regular meetings will take place every two weeks. - Defining the dimensions of stereotypes to be investigated and the possible metrics that can be processed from a machine learning perspective. - Exploring, managing and curating news corpora in French for stereotypes investigation, with a view to making them widely available to the community to favor reproducible research and comparison. - Studying and developing new computational models to process large number of texts to reveal stereotype bias in news. Make use of pretrained models for the task. - Evaluate the methods on curated focused corpus and apply it to the unseen real longitudinal corpus and analyze the results with the team. - Preparing articles for submission to peer-reviewed conferences and journals. - Organizing progress meetings and liaising between members of the team. The hired person will interact with PhD students, interns and researchers being part of the GenderedNews project. According to his/her background his/her own interests and in accordance with the project's objective, the hired person will have the possibility to orient the research in different directions. *Scientific Environment* The recruited person will be hosted within the GETALP teams of the LIG laboratory (https://lig-getalp.imag.fr/), which offers a dynamic, international, and stimulating environment for conducting high-level multidisciplinary research. The person will have access to large datasets of French news, GPU servers, to support for missions as well as to the scientific activities of the labs. The team is housed in a modern building (IMAG) located in a 175-hectare landscaped campus that was ranked as the eighth most beautiful campus in Europe by the Times Higher Education magazine in 2018. The person will also closely work with Gilles Bastin (PACTE, a Sociology lab in Grenoble) and Ange Richard (PhD at LIG and PACTE). The project also includes an informal collaboration with 'Prenons la une' (https://prenonslaune.fr/) a journalists’ association which promotes a fair representation of women in the media. *Requirements* The candidate must have a PhD degree in Natural Language Processing or computer science or in the process of acquiring it. The successful candidate should have - Good knowledge of Natural Language Processing - Experience in corpus collection/formatting and manipulation. - Good programming skills in Python - Publication record in a close field of research - Willing to work in multidisciplinary and international teams - Good communication skills - Good mastering of French is required *Instructions for applying* Applications will be considered on the fly and must be addressed to François Portet (Francois.Portet@imag.fr). It is therefore advisable to apply as soon as possible. The application file should contain - Curriculum vitae - References for potential letter(s) of recommendation - One-page summary of research background and interests for the position - Publications demonstrating expertise in the aforementioned areas - Pre-defense reports and defense minutes; or summary of the thesis with the date of defense for those currently in doctoral studies *References* Deshpande et al. (2022). StereoKG: Data-Driven Knowledge Graph Construction for Cultural Knowledge and Stereotypes. arXiv preprint arXiv:2205.14036. Choenni et al. (2021). Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you? arXiv preprint arXiv:2109.10052. Nadeem et al. (2020) StereoSet: Measuring stereotypical bias in pretrained language models. ArXiv. Nangia et al. (2020) CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models. In EMNLP2020.
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| 6-29 | (2023-09-04) Project Manager@ELDA, Paris, France The Evaluations and Language resources Distribution Agency (ELDA), a company specialized in Human Language Technologies within an international context is currently seeking to fill an immediate vacancy for the permanent position: Project Manager - Intellectual Property, Personal Data Protection for AI and Language Technologies. Job description Under the CEO’s supervision, the Project Manager will handle legal issues related to the compilation, use and distribution of language datasets in a European and international environment. This yields excellent opportunities for creative, and motivated candidates wishing to participate actively in the Language Engineering field.
Their main tasks will consist of:
The position is based in Paris. Salary: Commensurate with qualifications and experience (between 40-60K€). Other benefits: complementary health insurance and meal vouchers. Required profile:
About
ELDA is an SME established in 1995 to promote the development and exploitation of Language Resources (LRs). Language Resources include all data necessary for language engineering, such as monolingual and multilingual lexica, text corpora, speech databases and terminology. ELDA’s role is to produce LRs, to collect and to validate them and, foremost, make them available to users in compliance with applicable regulations and ethical requirements. For further information about ELDA, visit: http://www.elda.org Applicants should email a cover letter addressing the points listed above together with a curriculum vitae to: ELDA
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| 6-30 | (2023-09-08) 2 Research and Teaching Associates – PhD Positions –Signal Processing and Speech Communication Laboratory (TU Graz), Austria The Signal Processing and Speech Communication Laboratory (https://www.spsc.tugraz.at) of
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| 6-31 | (2023-09-10) PhD position, MIAI, Université de Grenoble, France Job Offer: PhD Self-supervised models for transcribing the spontaneous speech of for 3- to 6-year-old children in French Starting date: between October 1st and December 1st, 2023 (flexible) Application deadline: From now until the position is filled Interviews: from September or latter if the position is still open Salary: ~2000€ gross/month (social security included) Mission: research oriented (teaching possible but not mandatory) Place of work: Laboratoire d'Informatique de Grenoble, CNRS, Grenoble,France Keywords: deep learning, natural language processing, speech recognition for children's voices, documentation of language development Description As part of the Artificial Intelligence & Language Chair at the Multidisciplinary Institute in Artificial Intelligence (; https://miai.univ-grenoble-alpes.fr/research/chairs/perception-interaction/artificialintelligence-language-850480.kjsp?RH=6499588038450843), we offer a PhD thesis topic devoted to the enriched automatic transcription of the spontaneous speech of 3- to 6-year-old children using an architecture based on self-supervised models [1]. These methods have emerged as one of the most successful approaches in artificial intelligence (AI), as they allow to exploit a colossal amount of existing unlabeled data and so achieve significantly higher performance for many domains. As part of the DyLNet project (Language dynamics, linguistic learning, and sociability at preschool: benefits of wireless proximity sensors in collecting big data ; https://dylnet.univ-grenoblealpes.fr/), coordinated by A. Nardy, a children's speech collection was carried out in a socially mixed preschool over a period of 2 and a half years [2]. Each year, around 100 children wore a box fitted with microphones that continuously recorded their speech. These boxes were worn for one week a month. We thus collected ~ 30,000 hours of recordings, 815 of which were transcribed and annotated by linguists. This unprecedentedly large corpus of children's spoken French will enable to meet the technical challenges associated with automatic speech processing. While continuous and unsupervised collection methods are now available, another challenge is the automatic transcription of children's voices, made difficult by their acoustic characteristics. The aim of the thesis is to design a transcription system for researchers as well as child development professionals (teachers, speech therapists, etc.). The aim of the thesis is therefore to: - review the state-of-the-art models and the performances achieved by automatic transcription tools for children's voices - implement processes to exploit the mass of audio data collected, and the associated metadata (sociodemographic information on participants, contexts of enunciation, interlocutors, etc.). - design and develop a system for transcribing children's speech using self-supervised tools, as proposed by Speechbrain [3]. The best system obtained will be made available to the language acquisition research community and child development professionals. - set up a system evaluation protocol based on transcribed data - propose tools for automating some of the linguistic analyses to enrich the obtained transcriptions and document oral language development in 3- to 6-year-old children. Skills : Master degree in Computer Science, Artificial Intelligence or Data Science Mastering Python programming and deep learning frameworks. Experience in automatic natural language processing will be really appreciated Excellent communication skills in French or, failing that, in English Scientific environment : The PhD. position will be co-supervised by Benjamin Lecouteux, Solange Rossato (LIG, Univ. Grenoble Alpes) et Aurélie Nardy (Lidilem, Univ. Grenoble Alpes). The recruited person will be part of the GETALP team of the LIG laboratory (https://lig-getalp.imag.fr/) which has extensive expertise and experience in the field of Natural Language Processing. The GETALP team offers a stimulating, multinational working environment, and provides the resources needed to complete the thesis in terms of equipment and scientific exchanges. Regular meetings with the three supervisors will take place throughout the thesis. Instructions for applying Application forms must contain: CV + letter/message of motivation + master + notes + be ready to provide letter(s) of recommendation. They should be addressed to Benjamin Lecouteux (benjamin.lecouteux@univ-grenoble-alpes.fr), Solange Rossato (solange.rossato@univ-grenoble-alpes.fr) and Aurélie Nardy (aurelie.nardy@univ-grenoblealpes.fr). [1] Evain, S., Nguyen, H., Le, H., Boito, M. Z., Mdhaffar, S., Alisamir, S., ... & Besacier, L. (2021). Lebenchmark: A reproducible framework for assessing self-supervised representation learning from speech. https://doi.org/10.48550/arXiv.2104.11462 [2] Nardy, A., Bouchet, H., Rousset, I., Liégeois, L., Buson, L., Dugua, C., Chevrot, J.-P. (2021). Variation sociolinguistique et réseau social : constitution et traitement d’un corpus de données orales massives. Corpus, 22 [en ligne]. https://doi.org/10.4000/corpus.5561 [3] Ravanelli, M., Parcollet, T., Plantinga, P., Rouhe, A., Cornell, S., Lugosch, L., ... & Bengio, Y. (2021). SpeechBrain: A general-purpose speech toolkit. https://arxiv.org/abs/2106.0462
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| 6-32 | (2023-09-12) Assistant Professor (tenure track) position, University of Rochester, NY, USA Anticipated Start Date: (Mid August, 2024) DETAILED JOB DESCRIPTION: The Department of Psychology at the Rochester Institute of Technology (RIT; www.rit.edu/psychology) invites candidates to apply for a tenure-track Assistant Professor position starting in August 2024. We are seeking an energetic and enthusiastic psychologist who will serve as an instructor, researcher, and mentor to students in our undergraduate (Psychology, Neuroscience) and graduate programs (Masters in Experimental Psychology, Ph.D. in Cognitive Science). We are particularly looking to build a cohort of faculty who can contribute to the interdisciplinary Ph.D. program in Cognitive Science and contribute to research, mentoring, and teaching using computational and laboratory methods. Candidates should have expertise in an area of Cognitive Science such as cognitive or behavioral neuroscience, AI, computational/psycho-linguistics, cognitive psychology, comparative psychology, or related areas. We are particularly interested in individuals whose area of research expertise expands the current expertise of the faculty. Candidates who can teach courses in natural language processing or computational modeling courses are especially encouraged to apply. The Department of Psychology at RIT serves a rapidly expanding student population at a technical university. The position requires a strong commitment to teaching and mentoring, active research and publication, and a strong potential to attract external funding. Teaching and research are priorities for faculty at RIT, and all faculty are expected to mentor students through advising, research and in-class experiences. The successful candidate will be able to teach courses in our undergraduate cognitive psychology track (Memory & Attention, Language & Thought, Decision Making, Judgement & Problem Solving), will be expected to teach research methods/statistics courses at the undergraduate and graduate level, and teach and mentor students in our graduate programs. In addition, candidates must be able to do research and work effectively within the department’s existing lab space. RIT provides many opportunities for collaborative research across the institute in many diverse disciplines such as AI, Digital Humanities, Human-Centered Computing, and Cybersecurity. We are seeking individuals who have the ability and interest in contributing to a community committed to student-centeredness; professional development and scholarship; integrity and ethics; respect, diversity and pluralism; innovation and flexibility; and teamwork and collaboration. Select to view links to RIT’s core values, honor code, and statement of diversity. THE COLLEGE/ DEPARTMENT: The Department of Psychology at RIT offers B.S., M.S. degrees, Advanced Certificates, minors, immersions, electives, and a new interdisciplinary Ph.D. degree program in Cognitive Science. The B.S. degree provides a general foundation in psychology with specialized training in one of five tracks: biopsychology, clinical psychology, cognitive psychology, social psychology, and developmental psychology. The M.S. degree is in Experimental Psychology, with an Advanced Certificate offered in Engineering Psychology. We offer accelerated BS/MS programs with AI, Sustainability, and Experimental Psychology. The Ph.D. degree is in Cognitive Science and the program is broadly interdisciplinary with several partner units across the university. We also offer joint B.S. degrees in Human Centered Computing and Neuroscience. The College of Liberal Arts is one of nine colleges within Rochester Institute of Technology. The College has over 150 faculty in 13 departments in the arts, humanities and social sciences. The College currently offers fourteen undergraduate degree programs and five Master degrees, serving over 800 students. THE UNIVERSITY: Founded in 1829, Rochester Institute of Technology is a diverse and collaborative community of engaged, socially conscious, and intellectually curious minds. Through creativity and innovation, and an intentional blending of technology, the arts and design, we provide exceptional individuals with a wide range of academic opportunities, including a leading research program and an internationally recognized education for deaf and hard-of-hearing students. Beyond our main campus in Rochester, New York, RIT has international campuses in China, Croatia, Dubai, and Kosovo. And with more than 19,000 students and more than 125,000 graduates from all 50 states and over 100 nations, RIT is driving progress in industries and communities around the world. Find out more at www.rit.edu . REQUIRED MINIMUM QUALIFICATIONS:
HOW TO APPLY: Apply online at http://careers.rit.edu/faculty; search openings, then Keyword Search 8262BR. Please submit your application, curriculum vitae, cover letter addressing the listed qualifications and upload the following attachments:
You can contact the chair of the search committee, Caroline DeLong, Ph.D. with questions on the position at: cmdgsh@rit.edu. Review of applications will begin October 1, 2023 and will continue until an acceptable candidate is found.
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| 6-33 | (2023-09-15) Postdoc at IRISA, Rennes, France Team Expression at IRISA is hiring a post-doc for 18 months in Speech synthesis. For more information, just follow this link:
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| 6-34 | (2023-10-05) Professor at Saarland University, Saarbrücken, Germany The Department of Language Science and Technology of Saarland University
seeks to hire a Professor of Speech Science (W2 with tenure track to W3). For details see <https://www.uni-saarland.de/fileadmin/upload/verwaltung/stellen/Wissenschaftler/W2283_W2TTW3_Speech_Science.pdf>. --
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| 6-35 | (2023-09-23) Assistant or Associate Professor of Computer Science, UTEP, El Paso, Texas Assistant or Associate Professor of Computer Science, UTEP, El Paso, Texas
The University of Texas at El Paso (UTEP) invites applications for a tenured/tenure-track Associate Professor position and three tenure-track Assistant Professor Positions in Computer Science (CS) starting in fall 2024. We invite applicants from all areas of CS. For three of the positions (including the Associate Professor position), preference will be given to those with demonstrated expertise in Artificial Intelligence.
Candidates are expected to have a record of high-quality scholarship and should be able to demonstrate the potential for excellence in both research and teaching. The department values both interdisciplinary research and industry/government experience.
More information and application instructions are at https://www.utep.edu/cs/news/news-2023/assistantassociateprofessor.html
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| 6-36 | (2023-09-25) Professor (W2 with Tenure Track to W3) for Speech Science (m|f|x) ar Saarland University, Saarbrücken, Germany Saarland University is a campus-based university with a strong international focus and a research-oriented profile. Numerous research institutes on campus and the systematic promotion of collaborative projects make Saarland University an ideal environment for innovation and technology transfer. To further strengthen this excellence in research and teaching, the Department of Language Science and Technology seeks to hire a
Professor (W2 with Tenure Track to W3) for Speech Science (m|f|x) Reference n° W2283 Six-year tenure track position, starting April 2025, with the possibility of promotion to a permanent professorship (W3). We are looking for a highly motivated researcher in the field of phonetics, speech science, and speech technology, with extensive knowledge of speech production, perception and acoustics. The successful candidate is expected to have expertise in experimental and computational approaches to research on spoken language. A focus on spoken dialog and conversational speech and/or multimodal aspects of communication is particularly welcome. The Department of Language Science and Technology is internationally recognized for collaborative and interdisciplinary research, and the successful candidate is expected to contribute to relevant joint research initiatives. A demonstrated ability to attract external funding of research projects is therefore highly desired. Phonetics, speech science and speech technology are core elements of our study programs on the M.Sc. and B.Sc./B.A. level, and the successful candidate is expected to teach the associated courses within these programs. What we can offer you: Tenure track professors (W2) have faculty status at Saarland University, including the right to supervise Bachelor’s, Master’s and PhD students. The successful candidate will focus on carrying out world-class research, will lead their own research group, and will undertake teaching and supervision responsibilities. Tenure track professors (W2) with outstanding performance will receive tenure as a full professor (W3) provided a positive tenure evaluation is made. Decisions regarding tenure are made no later than six years after taking up the tenure track position. The position offers excellent working conditions in a lively and international scientific community. Saarland University is one of the leading centers for language science and computational linguistics in Europe, and offers a dynamic and stimulating research environment. The Department of Language Science and Technology organizes about 100 research staff in nine research groups in the fields of computational linguistics, psycholinguistics, phonetics and speech science, speech processing, and corpus linguistics (https://www.uni-saarland.de/en/department/lst.html). The department serves as the focal point of the Collaborative Research Center 1102 'Information Density and Linguistic Encoding' (http://www.sfb1102.uni-saarland.de). It is part of the Saarland Informatics Campus (https://saarland-informatics-campus.de/en), which brings together 800 researchers and 2,000 students from 81 countries and collaborates closely with world-class research institutions on campus, such as the Max Planck Institute for Informatics, the Max Planck Institute for Software Systems, and the German Research Center for Artificial Intelligence (DFKI). Qualifications: The appointment will be made in accordance with the general provisions of German public sector employment law. Applicants will have a PhD or doctorate in an appropriate subject and will have demonstrated a proven track record of independent academic research (e.g. as a junior or assistant professor, or by having completed an advanced, post-doctoral research degree (Habilitation) or equivalent academic activity at a university or research institution). They will typically have completed a period of postdoctoral research and have teaching experience at the university level. They must have demonstrated outstanding research capabilities and have the potential to successfully lead their own research group. The successful candidate will be expected to actively contribute to departmental research and teaching, including introductory lectures in phonetics and phonology, speech science, as well as more advanced lectures. The teaching language is English (in the MSc programs) and German (in the BSc/BA programs). We expect that the successful candidate has, or is willing to acquire within an appropriate period, sufficient proficiency to teach in both languages. Your Application: Applications should be submitted online at www.uni-saarland.de/berufungen. No additional paper copy is required. The application must contain: • a cover letter and curriculum vitae (including phone number and email address) • a full list of publications • a full list of third-party funding (own shares shown) • your proposed research plan (2-5 pages) • a teaching statement (1 page) • copies of your degree certificates • full-text copies of your 5 most important publications • a list of 3 academic references (including email addresses), at least one of whom must be a person who is outside the group of your current or former supervisors or colleagues. Applications must be received no later than 12 October 2023. The search committee will decide in its first meeting on late applications. Please include the job reference number W2283 when you apply. Please contact crocker@lst.uni-saarland.de if you have any questions. Saarland University regards internationalization as an institution-wide process spanning all aspects of university life and it therefore encourages applications that align with its internationalization strategy. Members of the university's professorial staff are therefore expected to engage in activities that promote and foster further internationalization. Special support will be provided for projects that continue with or expand on collaborative interactions within existing international cooperative networks, e.g. projects with partners in the European University Alliance Transform4Europe (www.transform4europe.eu) or the University of the Greater Region (www.uni-gr.eu). Saarland University is an equal opportunity employer. In accordance with its affirmative action policy, Saarland University is actively seeking to increase the proportion of women in this field. Qualified women candidates are therefore strongly encouraged to apply. Preferential consideration will be given to applications from disabled candidates of equal eligibility. When you submit a job application to Saarland University you will be transmitting personal data. Please refer to our privacy notice (https://www.uni-saarland.de/verwaltung/datenschutz/) for information on how we collect and process personal data in accordance with Art. 13 of the General Data Protection Regulation (GDPR). By submitting your application, you confirm that you have taken note of the information in the Saarland University privacy notice.
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| 6-37 | (2023-10-02) PhD position at IMT Atlantique, Brest, France PhD Title: Summarization of activities of daily living using sound-based activity recognition
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| 6-38 | (2023-10-04) Transcripteurs de langue tchèque @ELDA, Paris, France Dans le cadre de ses activités de production de ressources linguistiques, ELDA recherche des transcripteurs (f/h) de langue maternelle tchèque à temps plein ou partiel pour la transcription de 1500 heures d’enregistrements audio et/ou la révision des transcriptions. Le nombre total d'heures à transcrire ou à réviser sera adapté selon les disponibilités du candidat ou de la candidate. La mission aura lieu dans les locaux d'ELDA (Paris 13e) ou à distance via un espace sécurisé. La mission peut démarrer dès à présent. ELDA (Agence pour la Distribution des ressources Linguistiques et l'Evaluation)
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| 6-39 | (2023-10-04) Transcripteurs de langue estonienne@ELDA, Paris, France Dans le cadre de ses activités de production de ressources linguistiques, ELDA recherche des transcripteurs (f/h) de langue maternelle estonienne à temps plein ou partiel pour la transcription de 1500 heures d’enregistrements audio et/ou la révision des transcriptions. Le nombre total d'heures à transcrire ou à réviser sera adapté selon les disponibilités du candidat ou de la candidate. La mission aura lieu dans les locaux d'ELDA (Paris 13e) ou à distance via un espace sécurisé. La mission peut démarrer dès à présent. ELDA (Agence pour la Distribution des ressources Linguistiques et l'Evaluation)
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| 6-40 | (2023-10-14) Internship @ Orange, Lannion, France Dans le cadre de ses activités de développement de technologies vocales pour l'Afrique subsaharienne, l'entité DATA&AI d'Orange Innovation propose aux étudiants en 2è année de Master un stage de recherche de 6 mois.
Ce stage a pour objectif le développement de systèmes de compréhension automatique de la parole de bout-en-bout pour des langues africaines (End-to-End Spoken Language Understanding systems in Sub-Saharan African Languages) et pourra débuter dès le mois de janvier 2024. Le stage se déroulera dans les locaux d'Orange Innovation à Lannion.
Pour davantage d'informations sur l'offre et candidater, rendez-vous sur cette page : https://orange.jobs/jobs/v3/offers/129806?lang=fr
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| 6-41 | (2023-10-16) Ingenieur d'études, DDL, Université de Lyon, France Le laboratoire DDL de l l'Université de Lyon recherche un ingénieur d'étude pour une durée de 4 mois. L'offre est accessible au lien suivant : https://emploi.cnrs.fr/Offres/CDD/UMR5596-MELCAN-001/Default.aspx
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| 6-42 | (2023-10-16) Fully funded PhD positions @ University of Colorado Boulder, CO,USA The Human Bio-Behavioral Signals (HUBBS) Lab at University of Colorado Boulder is seeking outstanding candidates for fully funded positions within the Ph.D. program in Computer Science, specializing in affective computing, human-computer interaction, biomedical health analytics, and human-centered machine learning.
Ideal candidates should possess the following qualifications: - Proficiency in data analytics and/or machine learning - Prior research experience in machine learning or human-computer interaction, preferably prior publication(s) and/or a Masters (with thesis). - Prior experience in human-centered applications
Interested candidates should apply using the following link and list Dr. Theodora Chaspari as a potential Ph.D. advisor:
Join the Human Bio-Behavioral Signals (HUBBS) Lab at CU Boulder! The goal of HUBBS Lab is to make fundamental contributions to human-centered machine intelligence (ML) and to promote scientific advancement in trustworthy artificial intelligence (AI). This endeavor highly draws from interdisciplinary collaborations in health and psychological sciences, social sciences, and learning sciences, and leads to interdisciplinary scientific contributions. Main research areas include trustworthy and responsible human-centered AI, including dimensions of privacy-preservation, explainability, and fairness; human-AI teaming and collaborative decision-making; intelligent assistive interfaces for personalized user feedback in education and health; assistive computational technologies for combatting racism and bias; multimodal data analytics.
Join Our PhD Program in Computer Science at CU Boulder! We are committed to providing students with the best possible resources and support throughout their’ PhD journey. Our Ph.D. offer includes:
The Computer Science Department and CU Boulder have consistently risen in rankings over the years. We are the fastest-growing and largest department within the College of Engineering and Applied Science. The department is home to 50 tenured and tenure-track faculty plus 25 instructional faculty and now hosts nearly 1,700 undergraduate and 500 graduate students.
Highlights
We are committed to fostering a diverse, inclusive, and academically excellent community. At the college level, our undergraduate student population included 30% who self-identified as women, 27% who self-identified as persons of color, 15% who were first-generation college students, and 14% who were international students. Our graduate student body included 32% of students who self-identified as women, 16% who self-identified as a person of color, and 29% international students. In 2022, 26% of our tenured/tenure track faculty self-identify as women, and 22% self-identify as people of color.
Location, location, location! Boulder is located at the base of the Rocky Mountains in north-central Colorado. Boulder is consistently ranked as one of the happiest and healthiest places to live in the United States, known for its active, outdoorsy lifestyle and a strong sense of community. The city enjoys 300 days of sunshine annually and has over 150 miles of hiking and biking trails, along with easy access to skiing, rock climbing, and other outdoor activities. Major companies like Google, Microsoft, Ball Aerospace, and Lockheed Martin have offices in Boulder. The Denver metropolitan area, a 30-minute drive from Boulder, is a world-class city with an international airport with direct flights to Tokyo, Paris, London, and Mexico City.
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| 6-43 | (2023-10-18) Post doc researcher @ University of Glasgow, UK We are looking to recruit a postdoctoral researcher (up to 3 years) with experience in deep learning + multimodal information processing (vision +audio/text). The researcher will contribute to an exciting interdisciplinary project that will develop human-centric AI models for analysing complex, audiovisual data to understand diversity and inclusion in screen media.
Please find details and link to apply for the post here: https://www.jobs.ac.uk/job/DDG225/research-assistant-associate
Information queries can be sent to tanaya.guha@glasgow.ac.uk
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| 6-44 | (2023-10-15) Fully funded PhD positions at CUBES, University of Memphis, TN, USA The CUBES (Computational Understanding of Behaviors, Experiences, and Subjective states) Lab at the University of Memphis is seeking outstanding candidates for fully funded positions within the PhD program in Computer Science. We are looking for students interested in research at the intersection of machine learning, psychology, and signal processing and specializing in affective computing, human-computer interaction, and ethical human-centered machine learning.
Ideal candidates should have experience in: - Data analytics, machine learning, and/or trustworthy and fair AI - Human-centered applications either in research or industry - Research communication (e.g., publications, presentations, master’s thesis)
Interested candidates should apply to the PhD program at UofM and list Dr. Brandon Booth as a potential PhD advisor. Priority screening of applications for the Spring 2024 term will begin on November 1st, 2023, and applications will be accepted through December 1st, 2023. We particularly welcome candidates from groups that are historically underrepresented in computer science.
Join the CUBES Lab at UofM! The goal of CUBES Lab is to use multimodal AI to understand, track, and promote beneficial mental states/experiences/behaviors in real-life settings without perpetuating group biases. We conduct basic research to realize informative and interpretable representations of experiences, and we aim to close the loop in interactive experience monitoring, scale up experience modeling systems, and find unique ways for human-AI teams to succeed where either alone may fail. Our work is inclusive of new ideas and directions, especially in interdisciplinary and translational research in education, health, procedural fairness, and other prosocial domains.
Join our PhD Program in Computer Science at UofM! The University of Memphis is a top-tier research university with a Carnegie R1 designation, and we are committed to engaging PhD students in cutting-edge research in their area of interest. Our PhD program builds both breadth (through core graduate courses) and depth (via a rich selection of advanced courses and requiring participation in research projects), and it provides opportunities to work with interdisciplinary teams on federally funded research collaborations. For example, CS faculty lead the NIH-funded mDOT Biomedical Technology Resource Center and the Center for Information Assurance (CfIA). In addition, CS faculty work closely with multidisciplinary centers at the university such as the Institute for Intelligent Systems (IIS).
Memphis Highlights Known as America’s Number 1 logistics hub, Memphis has been ranked as one of the “World’s Greatest Places” by TIME, as America’s 4th best city for jobs by Glassdoor, and 4th in “Best Cost of Living”. Memphis metropolitan area has a population of 1.3 million. It boasts a vibrant culture and has a pleasant climate with an average temperature of 63 degrees.
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| 6-45 | (2023-13-23) Ph position @ Saarland University, Germany : Machine Learning for Natural Languages and other sequence data, Saarland University, Germany ========================================================= ==== (Computer Science, Computational Linguistics, Physics or similar) The research group is focusing on getting a deeper understanding of how modern deep learning methods can be applied to natural languages or other sequence papers. Our recent achievements include a best paper award at COLING 2022 and a best theme paper award at ACL 2023. We offer a PhD position that is topically open and should have a strong focus on applying machine learning techniques to natural language data or other sequence data (e.g., string representations of chemical compounds). The ideal candidate for the position would have: 1. Excellent knowledge of machine learning and deep learning 2. Excellent programming skills 3. Masters degree in Computer Science, Computational Linguistics, Physics, or similar Salary: The PhD position will be 75% of full time on the German E13 scale (TV-L) which is about 3144€ per month before tax and social security contributions. The appointments will be for three years with a possible extension at 50%. About the department: The department of Language Science and Technology is one of the leading departments in the speech and language area in Europe. The flagship project at the moment is the CRC on Information Density and Linguistic Encoding. It also runs a significant number of European and nationally funded projects. In total, it has seven faculty and around 50 postdoctoral researchers and PhD students. The department is part of the Saarland Informatics Campus. With 900 researchers, two Max Planck institutes and the German Research Center for Artificial Intelligence, it is one of the leading locations for Informatics in Germany and Europe. How to apply: Please send us a letter of motivation, a research plan (max one page), your CV, your transcripts, if available a list of publications, and the names and contact information of at least two references, as a single PDF or a link to a PDF if the file size is more than 5 MB. Please apply latest by November 20th, 2023. Earlier applications are welcome and will be processed as they come in. Contact: Applications and any further inquiries regarding the project should be directed to
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| 6-46 | (2023-10-23) Post Doc position: Natural Language Processing, Saarland University, Germany Post Doc position: Natural Language Processing, Saarland University, Germany ============================================================= (Computer Science, Computational Linguistics or similar) The research group is focusing on getting a deeper understanding of how modern deep learning methods can be applied to natural languages. Our recent achievements include a best paper award at COLING 2022 and a best theme paper award at ACL 2023. We offer a PostDoc position that is topically open and should have a strong focus on applying machine learning techniques to natural language data. The research should on the one hand be connected to ongoing research of PhD students and at the same time pursue a clear new direction. The ideal candidate for the position would have: 1. Solid experience in natural language processing 2. Excellent knowledge of machine learning and deep learning 3. Be involved, knowledgeable and generous in scientific discussions with all group members 4. Excellent programming skills 5. Doctoral degree in Computer Science, Computational Linguistics or similar Salary: The PostDoc position will be 100% of full time on the German E13 scale (TV-L) which is about 4188€ per month before tax and social security contributions. The appointments will be for two years with a possible extension. About the department: The department of Language Science and Technology is one of the leading departments in the speech and language area in Europe. The flagship project at the moment is the CRC on Information Density and Linguistic Encoding. It also runs a significant number of European and nationally funded projects. In total, it has seven faculty and around 50 postdoctoral researchers and PhD students. The department is part of the Saarland Informatics Campus. With 900 researchers, two Max Planck institutes and the German Research Center for Artificial Intelligence, it is one of the leading locations for Informatics in Germany and Europe. How to apply: Please send us a letter of motivation, a research plan (max one page), your CV, your transcripts, a list of publications, and the names and contact information of at least two references, as a single PDF or a link to a PDF if the file size is more than 5 MB. Please apply latest by November 20th, 2023. Earlier applications are welcome and will be processed as they come in. Contact: Applications and any further inquiries regarding the project should be directed to dietrich.klakow at lsv.uni-saarland.de
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