Postdoc in neuroscience of reward learning

The Laboratory for Social and Neural Systems Research is a research center at the University of Zurich that investigates the principles underlying social and neuronal systems (https://www.zne.uzh.ch/en/facilities.html). The lab brings together scientists from the social and natural sciences (incl. neuroscience, economics, psychology, computer science, and physics) to study mechanistic links between neuronal circuits, human behavior and social interactions, using a combination of brain imaging methods, neuro-modulation techniques, behavioral experiments and computational approaches. A single custom-built unit hosts multiple state-of-the-art facilities that are fully dedicated to research. These facilities include a Siemens Healthineers Magnetom Cima.X scanner as well as laboratories for transcranial magnetic and electric stimulation, psychophysics, behavioral group studies, peripheral and autonomic neurophysiology and pharmacology. Additionally, we have access to a 7T high-field human MRI facility.

Postdoc in neuroscience of reward learning

The successful candidate will work with Prof. Todd Hare and other SNS Lab researchers on the neuro-computational basis of human learning and decision making using cutting-edge multimodal neuroimaging techniques. For example, the successful candidate will work on a project aimed to determine if reward learning and corresponding striatal responses incorporate causal knowledge during Pavlovian conditioning. Using theoretically grounded and empirically verified reward learning predictions – blocking, conditioned inhibition, and overexpectation – we will test how Pavlovian conditioning operates over stimuli that are known to be either causal generators or correlated predicters of reward outcomes. Knowledge of the causal mechanisms should, in principle, determine how a reward learning agent summates predictions over two or more conditioned stimuli and whether a specific conditioned inhibitor will block a given causal state. We will test these hypotheses using modified versions of the standard conditioned inhibition and overexpectation paradigms together with functional neuroimaging. The results of these studies will provide important new insights into the fundamental nature of reward learning. They will reveal if Pavlovian conditioning mechanisms, which are generally thought to be model-free, incorporate model-based knowledge of cause-and-effect relationships in the environment or if instead they remain agnostic about causality and operate via predictiveness alone.

Candidates should hold (or expect) a good PhD degree in a relevant discipline (e.g., neuroscience, psychology) and have published work using human neuroimaging methods (esp. fMRI). Candidates should have expertise with computational modelling of behavior and/or neural data and a high level of proficiency in using Python, R, or similar statistical computing languages. Enthusiasm for scientific work and good scientific practice is essential. Candidates should speak and write English proficiently. Applications are welcome from overseas candidates as well as Swiss and EU nationals.

We offer varied and interesting work in an inspiring and socially relevant environment. Diversity and inclusion are important to us.
The annual gross salary is CHF 91,659.

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