Connectionists: Postdoctoral Position: Neural Basis of Hierarchical Reinforcement Learning

[Yael Niv]

(apologies for the multiple postings -- this is the last of the batch  
of open positions!)

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 
.
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