Postdoctoral Fellow

Our Lab researches the computational neuroscience of reward prediction and learning. Broadly, we study how timing, inference processes, and goal selection modulate learning in reward-guided tasks in both animals and humans. We also investigate how reward-guided learning is influenced by neural activity in reward-learning circuitry within the brain. In combination, our goal is to understand how reward learning is altered by compulsive behaviors, addiction, and motivational deficits during disorders of mental health.

We are looking for a postdoc to utilize dynamical systems theory, reinforcement learning, and Bayesian inference to explore how goal-directed behaviors and learning are regulated by neural circuits. The ideal candidate will conceptualize the dynamic processes of reward prediction and learning as events that unfold across time and experience, and be fluent in both the theoretical and empirical aspects of behavioral and cognitive neuroscience. Ultimately, our aim is to link the normative models of behavior to the dynamics of reward learning circuits in the brain.

Foundationally, we are a computational lab that collaborates with a broad network of research groups spanning rodent, NHP, and human studies. We would suit a computational neuroscientist excited to work at the intersection of theory and experimentation who thrives in an interdisciplinary research environment.

Interested candidates should have strong quantitative and programming skills, fluency with dynamical systems theory (ideally in reinforcement learning and/or Bayesian inference), and a demonstrated familiarity with neuroscience and behavioral research. Candidates should also have a record of scientific contributions in an area relevant to our lab's mission.

U.S. citizens and permanent residents are eligible to apply. NIH welcomes foreign nationals with the exception of individuals from this list.