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ISCApad Archive  »  2024  »  ISCApad #309  »  Jobs  »  (2024-01-07) PhD position @ Laboratoire Bordelais de Recherche en Informatique (LaBRI), Talence, France

ISCApad #309

Saturday, March 09, 2024 by Chris Wellekens

6-36 (2024-01-07) PhD position @ Laboratoire Bordelais de Recherche en Informatique (LaBRI), Talence, France
  
Dans le cadre du projet PEPR Santé numérique 'Autonom-Health' (Health, behaviors and autonomous digital technologies), le groupe de recherche en parole et langage du Laboratoire Bordelais de Recherche en Informatique (LaBRI) recherche des candidats pour un poste de doctorant entièrement financé (36 mois).

Le projet 'Autonom-Health' est un projet collaboratif sur la santé numérique entre SANPSY, LaBRI, LORIA, ISIR et LIRIS. Le résumé du projet 'Autonom-Health' peut être trouvé à la fin de cet e-mail. Les missions qui seront abordées par les candidats retenus figurent parmi ces tâches, en fonction du profil du candidat :
- Tâches de collecte de données de parole :
- Définition de scénarios pour la collecte de discours spontanés à l'aide d'Agents Sociaux Interactifs (SIAs).
- Collecte d'interactions patient/médecin lors d'entretiens cliniques.
- Tâches liées à la reconnaissance automatique de parole :
- Évaluer et améliorer les performances de notre système ASR end2end ESPNET sur des données réelles en français spontané enregistrées à partir de sujets sains et de patients.
- Adaptation du système ASR au domaine des entretiens cliniques.
- Transcription phonétique automatique / alignement à l'aide d'architectures end2end.
- Adapter les transcriptions pour les utiliser avec les outils d'analyse sémantique développés au LORIA.
- Tâches d'analyse de la parole :
- Analyse des biomarqueurs vocaux pour différentes maladies : adaptation de nos biomarqueurs définis pour la somnolence, recherche de nouveaux biomarqueurs ciblés pour des maladies spécifiques.

Le poste sera hébergé au LaBRI, mais en fonction du profil du candidat, une collaboration étroite est attendue avec l'équipe 'Sémagramme' du LORIA (contact : Maxime Amblard maxime.amblard@loria.fr).
 
Salaire brut : environ 2044 €/mois 
Date de début : octobre 2024 
Qualifications requises : Master en traitement du signal / analyse de la parole / informatique 
Compétences : Programmation Python, apprentissage statistique (apprentissage machine, apprentissage profond), traitement automatique des signaux/de la parole, excellente maîtrise du français (interactions avec des patients et des cliniciens français), bon niveau d'anglais scientifique. 
Savoir-faire : Familiarité avec la boîte à outils ESPNET et/ou les outils d'apprentissage profond, connaissance de la conception de systèmes de traitement automatique de la parole. 
Compétences sociales : Bonne capacité à s'intégrer dans des équipes multidisciplinaires, capacité à communiquer avec des non-experts.
Candidatures : Pour postuler, veuillez envoyer par e-mail à jean-luc.rouas@labri.fr un seul fichier PDF contenant un CV complet, une lettre de motivation (décrivant vos qualifications, vos intérêts de recherche et votre motivation pour postuler), les coordonnées de deux référents et des copies de diplômes et relevés de notes (Master, Licence).


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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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