Title: Understanding Neural Mechanisms of Human Motor Learning by Using
Explainable AI for Time Series and Brain-Computer Interfaces
This PhD project will focus on uncovering mechanisms of human motor
adaptation by using advanced computational tools. By analyzing (and
potentially collecting new) EEG and MEG + behavioral data from multiple
datasets you will explore how the brain adapts movements to external
perturbations. There will also be an opportunity to test the newly
obtained understanding using a brain-computer interface (BCI) protocol.
The project will be co-supervised by Dr. Dmitrii Todorov and Dr.
Veronique Marchand-Pauvert, and will be carried out within an
international interdisciplinary team.
We welcome applicants with a Master’s degree (or equivalent) in
computational neuroscience or related fields (broadly speaking).
Programming skills are essential; prior experience with computational /
cognitive / motor neuroscience is a plus.
Keywords: motor adaptation, MEG/EEG analysis, time series machine
learning, brain computer interfaces, RNN, dynamical systems, oscillations
Full details can be found here:
or here
Best regards,
Dmitrii Todorov
Chaire Professeur Junior (assistant professor)
Neural Connectivity and Plasticity team
Laboratoire d’Imagerie Biomedicale ( INSERM 1146 )
15 rue de l’école de Médecine
75006 Paris