Applications are invited for a post-doctoral research position in the laboratory of Dr. Maxim Bazhenov at the University of California, San Diego to develop neuroscience inspired machine learning algorithms capable of continual learning and adapting to the novel situations and contexts. This project involves close collaboration with the experimental laboratory of Dr. Bruce McNaughton (UC Irvine). The ultimate goal of the work is to advance the knowledge of how human and animal brains learn from experience and apply these principles to the artificial systems to enable continuous learning without catastrophic forgetting.
The successful candidate will collaborate with a team of researchers to design neural network models of dynamic interactions between the hippocampus and neocortex during learning and memory consolidation based on experimental data. These models will be used to derive learning principles that can be combined with advances in artificial intelligence and machine learning. An ideal candidate should have experience in computational/theoretical neuroscience and a basic knowledge of machine learning, or, alternatively, experience in machine learning algorithms and some basic knowledge of neuroscience. Experience with hierarchical learning, reinforcement learning, and/or goal-directed decision-making would be particularly helpful.
The University of California offers excellent benefits. Salary is based on research experience. Applicants should send a brief statement of research interests, a CV and the names of three references to Maxim Bazhenov at mbazhenov@ucsd.edu
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