Topic: Distributed Learning

Develop communication-efficient, privacy-preserving distributed learning algorithms for streaming data in edge inference networks. The PRIDeL collaboration connects Inria, Simula and NYCU, combining theory or experiments on convergence, generalization, data freshness and differential privacy. The doctorate must be defended by 31 December 2026, and the appointment must provide a research environment distinct from the doctorate. The contract begins in France, with international visits encouraged. Appointment: 1 year, 4 months; advertised start 2027-01-01. 2 788 € gross monthly.

Brain-machine interfaces (BMIs) change how the brain sends and receives information from the environment, opening new ways to probe brain function. For instance, motor BMIs allow us to precisely define and manipulate the sensorimotor loop which has enabled new insights into motor control and learning. In this talk, I’ll first present an example study where sensory-motor loop manipulations in BMI allowed us to probe feed-forward and feedback control mechanisms in ways that are not possible in the natural motor system. This study shed light on sensorimotor processing, and in turn led to state-of