Postdoc Research Position in Deep Neural Network Approaches for Closed-Loop Deep Brain Stimulation
A postdoctoral position for the project “Deep Neural Network Approaches for Closed-Loop Deep Brain Stimulation Using Cortical and Subcortical Sensing” is available within the Brain Modulation Lab in the Department of Neurological Surgery at Massachusetts General Hospital. This 3-year position, to start as of January 2020, is funded through a U.S.-German Collaborative Research in Computational Neuroscience (CRCNS) R01 grant through NINDS to co-Principle Investigators Mark Richardson, MD, PhD (Neurosurgery, MGH) and Rob Turner, PhD (Neurobiology, University of Pittsburgh), and co-investigator Tom Mitchell (Machine Learning, Carnegie Mellon University). Our German collaborators are Andrea Kühn and Julian Neumann (Neurology) at Charité Universitätsmedizin Berlin and Benjamin Blankertz (Computer Science) at Technische Universität Berlin. In addition, the postdoctoral fellow will be an active participant in a new collaborative initiative in intracranial human neuroscience, between MIT’s Department of Brain and Cognitive Sciences and MGH Functional Neurosurgery, led by Dr. Richardson.
This CRCNS project builds upon the extensive and unique experiences with multi-day, extra-operative recording from DBS leads in patients at Charité Hospital and with intraoperative ECoG and DBS recording from patients at MGH. The overall goal of this project is to establish intelligent algorithms to identify physiological and pathophysiological states in ECoG data that predict epochs during which stimulation would facilitate movement or reduce symptoms. The postdoctoral associate will be expected to develop techniques for multi-feature classification from cortical and subcortical recordings, including real-time classification, using advanced machine learning algorithms, working closely with the whole research team. Candidates should have a PhD in computational neuroscience or machine learning, with an excellent productivity record in machine learning-related work. Previous work with human intracranial electrophysiology is strongly desired.
Interested applicants should send a cover letter and CV to email@example.com
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