About the research centre or Inria department

The Inria Sophia Antipolis – Méditerranée center counts 34 research teams as well as 8 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.”

Context

The project will take place within Epione team (Inria), located in the tech park of Sophia Antipolis (France).

The longstanding research activity of our group revolves around the analysis and treatment of biomedical data, with a focus in machine learning, medical imaging, computational anatomy and computational physiology. Over the past twenty years the group developed innovative approaches in image processing, statistical learning and patient-specific biophysical modeling, with translation to the clinical domain, and to the creation of several biotech startups.
This goal is accomplished through methodological, technical, and translational advances towards the development of a novel generation of modeling methods in biomedicine. The group is currently composed by 5 permanent researchers, several postdoc fellows and research engineers, and by more than 20 PhD students.

Assignment

This project 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. The project has a specific focus on the efficient federation of Bayesian non-parametric frameworks, such as Gaussian Processes and Bayesian deep neural networks, consistently with their probabilistic theoretical formulation. The project tackles the following scientific challenges:

• Methodological. Extending the federated paradigm to the Bayesian non-parametric setting, and developing novel scalable approaches to probabilistic modeling and prediction from distributed data.
• Technical. Developing our federated learning framework through a self-contained system that can be securely deployed across different centers and collaborators.
• 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.
  Complete information available at https://marcolorenzi.github.io/material/job_offer-PostDoc-FedBioMED.pdf

References:

• Santiago Silva, Boris Gutman, Barbara Bardoni, Paul M Thompson, Andre Altmann, Marco Lorenzi. Multivariate Learning in Distributed Biomedical Databases: Meta-analysis of Large-scale Brain Imaging Data. IEEE International Symposium on Biomedical Imaging (ISBI), Venice, 2019.
• Luigi Antelmi, Nicholas Ayache, Philippe Robert and Marco Lorenzi. Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data. Proceedings of the 36th International Conference on Machine Learning (ICML).
• Marco Lorenzi, Maurizio Filippone. Constraining the Dynamics of Deep Probabilistic Models. Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80:3233- 3242, 2018.
• Marco Lorenzi, Andre Altmann, Boris Gutman, et al. Susceptibility of brain atrophy to TRIB3 in Alzheimer’s disease: Evidence from functional prioritization in imaging genetics. Proceedings of the National Academy of Sciences of the United States of America (PNAS). March 20, 2018. 115 (12) 3162-3167.

Main activities

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;

Skills

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;

Strong communication abilities are necessary, as well as motivation in taking responsibilities (e.g. supervision, organization of scientific events).

Benefits package

• 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

Remuneration

• Gross Salary: 2653 € per month

Please click here to apply.