PhD Position in Secure Collaborative Learning for Health Data

This PhD position at Université Paris-Saclay, conducted in collaboration with CEA-LIST, focuses on advancing secure collaborative learning and deep learning applications in healthcare. The project addresses data sovereignty challenges by developing edge-to-cloud federated learning architectures incorporating fully homomorphic encryption and adversarial resilience. The candidate will deploy and optimize deep learning models, integrate large-scale models into collaborative learning settings, and design novel aggregation methods for non-IID data distributions. The successful applicant is expected to contribute actively to cutting-edge research in privacy-preserving medical AI.

• Deploy, monitor, and optimize deep learning models within decentralized and federated learning frameworks
• Integrate large-scale models into collaborative learning environments for healthcare applications
• Develop and evaluate novel data aggregation techniques tailored for non-IID data distributions
• Collaborate with researchers at CEA-LIST to implement secure computation protocols and end-to-end encryption methods
• Contribute to scientific publications and present research findings at international conferences