Project "FED-BIONET"

This project envisions a novel paradigm for machine learning in healthcare based on the innovative concept of federated learning. Our goal is to exploit the power of modern learning methods at full capacity within the current clinical data scenario. To this end, we will focus 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.

This project will provide the fellow with highly competitive skills for securing a position in the tech industry, in particular in startup and companies in the domain of machine learning and artificial intelligence. Furthermore, the strong biomedical application tackled during the PhD project will allow the student to acquire solid competences in biomedical data management and analysis. This aspect may open up important career perspectives in the field of biotech, pharmaceutical, and clinical research.

The project will count on the expertise and collaboration of the partners of the ENIGMA consortium, a worldwide network of clinical centers providing data and expertise in dementia research. The project will also involve a 6 months visit period to the Centre of Medical Image Computing (CMIC) of University College London (UCL).

• Marco Lorenzi Université Côte d'Azur, INRIA Sophia Antipolis, EPIONE Team
• Barbara Bardoni Université Côte d’Azur, INSERM, CNRS, IPMC