POSTDOCTORAL POSITION IN COMPUTATIONAL NEUROSCIENCE:
kESI – kernel-based method of electrophysiological source imaging
The goal of the project is to develop, implement and investigate, a new technique of source localization of specific events in neural activity, such as a seizure, from multimodal imaging data, including ECoG (strip and grid electrodes), depth electrophysiological recordings, and MRI data from humans. The method will be validated with ground truth model data and tested on clinical data obtained from epilepsy patients. The fellow will build on already existing tools and techniques developed in the lab, see especially Potworowski et al. 2012 and Ness et al. 2015.
The ideal candidate will have a strong computational background, experience in numerical solutions of PDEs, electrodynamics, computational neuroscience, or brain imaging. We can only accept candidates 0 to 7 years after reception of PhD (+1,5 year per child for mothers). If you have a solid background in physics, maths, computer science, or engineering, and want to apply it to understanding or curing the brain, this position might be of interest to you.
To apply or inquire send email to email@example.com. Applications should include a CV, statement of research interests, and the names and full contact details of one to three referees. To demonstrate your prowess in Python provide your github account or solutions to problems 1-4 provided here (at least two, the more the better). If you cannot satisfy these conditions but still think we should hire you, convince us.
The deadline for application is October 15th, 2017