PhD Position in AI-Based Prediction of Post-Stroke Motor Recovery Using Neuroimaging and Brain-Computer Interfaces

This PhD position at Université Grenoble Alpes focuses on advancing AI-driven rehabilitation for stroke patients. The candidate will leverage multimodal neuroimaging data—including ultra-high-field 7T fMRI, MEG, and ECoG—to build predictive models for motor recovery. The project aims to identify biomarkers for personalized rehabilitation and improve patient selection for brain-computer interface (BCI) therapies. Conducted at Clinatec (CEA Grenoble) in collaboration with LPNC (CNRS/Université Grenoble Alpes), the work bridges computational neuroscience, biomedical engineering, and AI. The successful applicant will contribute to developing clinical tools that enhance neuroplasticity understanding and rehabilitation outcomes.

• Develop machine learning and deep learning models (e.g., CNNs, transformers) to integrate multimodal neuroimaging data (7T fMRI, MEG, ECoG) for predicting motor recovery post-stroke.
• Characterize brain lesions, motor networks, and neural mechanisms underlying motor intention using advanced neuroimaging techniques.
• Validate predictive models using clinical datasets from ongoing BCI rehabilitation studies.
• Identify biomarkers for rehabilitation potential and develop personalized rehabilitation tools.
• Collaborate with clinical teams to ensure translational relevance of research outcomes.
• Publish findings in peer-reviewed journals and present results at international conferences.
• Contribute to understanding post-stroke neuroplasticity mechanisms.