Postdoctoral Position: Neural Basis of Hierarchical Reinforcement Learning

Postdoctoral Research Associate

We are seeking a postodoctoral research associate for a joint project
in Hierarchical Reinforcement Learning in the labs of Dr. Matthew
Botvinick and Dr. Yael Niv at the new Princeton Neuroscience Institute
and the Department of Psychology at Princeton University. The project
focuses on behavioral and fMRI experiments motivated by the theory of
Hierarchical Reinforcement Learning, investigating its implications
and bringing human decision making data to bear on new theoretical
advances (see http://www.princeton.edu/~yael/Publications/BotvinickNivBarto2008.pdf)
. We seek an exceptionally talented candidate with a strong background
in computational modeling and/or behavioral and functional imaging
experimentation, and a keen interest in learning and decision-making.
This is a one-year postdoctoral position with the possibility of
renewal.

Essential Qualifications

PhD in neuroscience, psychology, computer science, engineering, or
related field. All applications should include a cover letter stating
background and research interests, a CV, one or two representative
publications, and contact information of at least two references.

Preferred Qualifications

The ideal candidate will have a solid computational background
(machine learning, reinforcement learning, Bayesian models), will be
proficient in programming (Matlab or equivalent) and will have robust
experience with fMRI (event related designs and model-based analysis
techniques).

Princeton University is an equal opportunity employer and complies
with applicable EEO and affirmative action regulations. You may apply
online to Requisition Number: 0800730 at http://jobs.princeton.edu.
For general application information and how to self-identify, see http://www.princeton.edu/dof/ApplicantsInfo.htm