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: 

https://sdrive.cnrs.fr/s/TPwgCpG6z8rPwBR

or here

https://sites.google.com/view/dtodorov-neuro/