Connectionists: Seeking Postdoctoral Research Associate for NIDA-Princeton collaboration

[Yael Niv]

A postdoctoral research position is available, to support an ongoing collaboration between the labs of Dr Geoffrey Schoenbaum at the NIDA-IRP (https://irp.drugabuse.gov/schoenbaum.php) and Dr. Yael Niv at the Princeton Neuroscience Institute and Department of Psychology (http://www.princeton.edu/~nivlab). 

Research in the Schoenbaum lab utilizes rodent models to study learning and decision making, as well as addiction. Research in the Niv lab focuses on computational modeling of learning and decision making at the systems level (reinforcement learning, Bayesian inference), and model-driven behavioral and functional imaging experiments of human learning and decision making. Over the past 10 years, the labs have collaborated in a series of experiments that have leveraged rodent and single unit recording approaches and computational modeling to study the brain circuits mediating associative learning.

We seek an exceptionally talented candidate to continue this collaboration. Work will consist of experimental work in rodents, combined with computational modeling to analyze and interpret past results and to suggest new studies. For this, we are looking for someone who is intensely interested in and has proven experience in empirical investigations of associative learning and decision making, expertise in computer programming and modeling, and a desire to explore reinforcement learning and normative models of behavior. Interest in addiction is also valuable, but not essential. Anticipated start date can be as soon as spring of 2018, though there is considerable flexibility. This is a one-year position with the expectation of renewal, pending satisfactory performance.
                                                                                                                                                         
Essential Qualifications: PhD in psychology, neuroscience or equivalent. Proven experience with computer programming.

Preferred Qualifications: The ideal candidate will have significant experience with single unit recording (single unit and ensemble analytic techniques especially desired, in rodents or other species).  Experience with fMRI is also of interest. Proficiency in programming (Matlab, Python or equivalent) and experience with computational modeling (machine learning, reinforcement learning, Bayesian models) is highly desirable.

To apply, please send a cover letter stating background and research interests and citations of at least two representative publications, a CV, and contact information for at least two references to geoffrey.schoenbaum at nih.gov