Applications are invited for a postdoctoral position focused on computational modeling of human neurophysiology systems in the Department of Neurological Surgery at UCSF. The incumbent will contribute to clinical research and medical device technology development projects by spearheading advanced data analysis under the general direction of Dr. Edward Chang, working closely with Dr. Maryam Shanechi of the University of Southern California and other collaborators. The position will entail working independently and in cooperation with team members to analyze multimodal time-series data from human neurophysiological and behavioral studies, with a special emphasis on developing models, state estimators, and closed-loop control algorithms for neuralinterface-based clinical intervention. The incumbent will develop, formalize, test, and critically assess approaches for system identification, state prediction, and closed-loop control for symptom alleviation and disease modification, supporting the validation of these approaches in patients in collaboration with clinical research team members. The incumbent will also work closely with software engineers to implement computational methodologies in next-generation neural interface device systems.

 

 

The successful candidate will join a dynamic and engaged research group focused on neuromodulation to address complex network-based brain disorders including chronic pain and major depression. In addition to ongoing clinical trials utilizing advanced brain stimulation approaches, our multidisciplinary clinical neuroscience group conducts intracranial electroencephalography (iEEG) and behavioral studies to characterize disease-related brain networks and understand their response to stimulation-based intervention. Our broader team includes longstanding collaborations with specialist physician-researchers and field-leading neural engineers.

 

 

Applicants should have established expertise in statistical signal processing and inference and/or control theory. Experience with neurophysiological datasets would be advantageous.

 

 

 

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