Alumni Job Opportunities
Post-doctoral fellow in computational/theoretical neuroscience
The Chang Lab at UCSF and the Sommer Lab at UCB are jointly seeking two (2) post-doctoral fellows in computational/theoretical neuroscience. Our groups are developing and applying novel computational methods to understand population neural data. The positions have the specific focus on understanding the dynamics of neural population data collected directly from the cortical surface of humans during the perception and production of speech at multiple temporal resolutions.
These positions are fully funded as part a collaborative effort between University of California San Francisco, University of California Berkeley, and Lawrence-Berkeley National Laboratory. This Bay Area collaboration combines UCSF's strong clinical and experimental neuroscience experience with in vivo cortical electrophysiology, UCB's expertise in theoretical neuroscience, statistical learning and data analysis, and LBNL's supercomputing and applied mathematics together with electronics and microfabrication. Successful applicants will have access to the full range of facilities and expertise uniquely allowed by this collaboration.
Applicants should have a Ph.D. (or equivalent degree) in a quantitative field such as computer science, electrical engineering, physics, or applied mathematics/statistics with a strong publication record. Expertise in theoretical/computational neuroscience or the analysis/modeling of complex dynamical systems is recommended; experience in analyzing the dynamics of population neural recordings is preferred. Applicants must be able to work in a collaborative, multi-disciplinary environment and exhibit good communication skills. Experience with scientific programming languages (e.g. C/C++, python, Matlab) is required. Expertise in speech/language and/or high-performance computing is not required, but is beneficial.
To apply, please submit a CV and three letters of reference to:
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