2022-05159 - Post-Doctoral Research Visit F/M Federated Learning in Healthcare (FLH): Analysis of Multicentric Hospital Data
Le descriptif de l’offre ci-dessous est en Anglais

Type de contrat : CDD

Niveau de diplôme exigé : Thèse ou équivalent

Fonction : Post-Doctorant

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

The project FLH is a collaboration between the EPIONE group of Inria Sophia Antipolis and the startup MyDataModels (MDM).

In this project, we focus on the use of federated learning for the analysis of healthcare data issued from a network of hospitals and clinical collaborators. The project is based on the development of unsupervised learning tools for the analysis of data from consultation software in general medicine.

Specific scientific and technological challenges of the project are:

  • Design of learning methods for the analysis of highly heterogeneous data, either quantitative and structured (clinical/biological examinations), or unstructured (reports). This challenge requires the investigation of new tools for the joint analysis of complex data, based on dimensionality reduction approaches, with the analysis of language data (natural language processing - NLP -).
  • Technological development to adapt the Fed-BioMed software (https://fedbiomed.gitlabpages.inria.fr/). In particular, the software must take into account the non-interoperability of the systems, and the heterogeneous nature of the data format in each establishment.


Mission confiée

The project requires to acquire a solid understanding of the medical data and use-cases provided by the clinical partners, and to develop appropriate machine learning/statistical methods for tackling the analysis problem.

The candidate is expected to implement and deploy the model within the Fed-BioMed federated learning frameworks, to collaborate with the dev team, and contribute to the software development.

The project requires communication and interaction with research, industrial, and clinical personnel.

Principales activités

During the project, the candidate will be in charge of:

  • Mastering the federated learning tools and related methodology
  • Contacting and interacting with the clinical partners to identify the optimal solution for remote data access and managment 
  • Development of proof-of-concept methods based on locally available datasets
  • Mastering advanced machine learning tools for the analysis of medical data
  • Deploying Fed-BioMed software in the hospitals network
  • Technical testing of the federated architecture  
  • Executing of federated learning and validation of the model on external data
  • Writing and publishing scientific work
  • Writing and publishing documentation and open-source software in Fed-BioMed


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

  • Statistics, Bayesian Modeling;
  • Natural Language Processing;
  • 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 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
  • Supplementary social protection


Gross Salary: 2653 € per month