We are looking for a talented postdoc to join our team at Inria!
What is the project about?
We have recently proposed proxy measures of brain health derived from brain imaging and electrophysiology (https://doi.org/10.7554/eLife.54055). In subsequent work we have shown that MEG and EEG can be equally powerful for building electrophysiological proxy measures, potentially opening the door to clinical translation (https://doi.org/10.1016/j.neuroimage.2020.116893) into patient populations in which neither MRI nor high-density EEG is available. To unleash the full potential of electrophysiological proxy measures of brain health, it is necessary to build models that work robustly in many different situations and across different datasets (https://doi.org/10.1093/brain/awy251).
This project focuses on tackling the generalisation gap of proxy measure models when moving between different datasets and with that, between different acquisition protocols and recording devices.
Who are we looking for?
The successful candidate has recently completed their PhD in empirical quantitative research (medical physics, biology, neuroscience, experimental psychology or related field) and a strong interest in computational statistics and neuroscience.
The candidate can present strong evidence for their mastery of the English language (writing, speaking). French language skills are not required.
Prior experience in analysis of EEG or MEG is a major asset, as it will allow the candidate to immediately focus and advance on the project.
Working knowledge of scientific computing in Python (NumPy, Scipy) or R is required. Knowledge in both Python and R is a plus.
The principal work will be done in Python based on standard machine learning libraries (scikit-learn) and the MNE software (https://mne.tools) for processing MEG and EEG.
The successful candidate will work with us, not for us!
The position starts once we have found the right candidate.
Denis Engemann & Alexandre Gramfort