2021-03964 - Post-Doctoral Research Visit F/M Federated Statistical Learning for New Generation Meta-Analyses of Large-scale and Secured Biomedical Data (Fed-BioMED)
Le descriptif de l’offre ci-dessous est en Anglais

Contrat renouvelable : Oui

Niveau de diplôme exigé : Thèse ou équivalent

Fonction : Post-Doctorant

Niveau d'expérience souhaité : Jusqu'à 3 ans

A propos du centre ou de la direction fonctionnelle

The Inria Sophia Antipolis - Méditerranée center counts 34 research teams as well as 7 support departments. The center's staff (about 500 people including 320 Inria employees) is made up of scientists of different nationalities (250 foreigners of 50 nationalities), engineers, technicians and administrative staff. 1/3 of the staff are civil servants, the others are contractual agents. The majority of the center’s research teams are located in Sophia Antipolis and Nice in the Alpes-Maritimes. Four teams are based in Montpellier and two teams are hosted in Bologna in Italy and Athens. The Center is a founding member of Université Côte d'Azur and partner of the I-site MUSE supported by the University of Montpellier.

Contexte et atouts du poste

FED-BioMED focuses on methodological, technical, and translational advances towards the development of a novel generation of federated learning methods for the analysis of private and large-scale multi-centric biomedical data.

Mission confiée

The project has a specific focus on the efficient federation of frameworks robust to data heterogeneity and uncertainty, and tackles the following scientific challenges:

 - Methodological. Extending the federated paradigm to novel scalable approaches to probabilistic modeling and prediction from siloed data.

- Technical. Developing our federated learning framework through a self-contained system that can be securely deployed across different centers and collaborators (fedbiomed.gitlabpages.inria.fr).

- Translational. Demonstrating federated learning on two applications: 1) Discovering novel genetic underpinnings of neurological and psychiatric disorders, and 2) Predictive modeling of sudden cardiac death from multi-centric imaging and clinical information.

Principales activités

During the project the candidate will:

  • Develop learning methods for federated analysis for private and distributed data;
  • Develop a formalism for federated learning in Bayesian non-parametric modeling;
  • Deploy advanced statistical learning methods into a wide range of biomedical/clinical applications;
  • Interact with INRIA students and researchers, and participate to the scientific life of the group;


Demonstrable experience in some of the following topics (the more the better):

  • Statistics, Bayesian Modeling;
  • Optimization, Distributed Computing;
  • Python and PyTorch/TensorFlow;
  • Biomedical Data Analysis;
  • Signal Processing;




  • Subsidized meals
  • Partial reimbursement of public transport costs
  • Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
  • Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
  • Professional equipment available (videoconferencing, loan of computer equipment, etc.)
  • Social, cultural and sports events and activities
  • Access to vocational training
  • Social security coverage


Gross Salary: 2653 € per month