Connectionists: Postdoctoral position in computational neuroscience and machine learning

[Maxim Bazhenov]

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 ofneuroscience. 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 at ucsd.edu

-- 
Maxim Bazhenov, Ph.D.
Professor, Department of Medicine,
Neurosciences Graduate Program,
UCSD, School of Medicine
http://www.bazhlab.ucsd.edu/

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