Postdoctoral Position in Computational Psychiatry and Data Science at Yale University

Yale University is seeking a highly motivated postdoctoral associate to join the multidisciplinary IMPACT-Y (Individually Measured Phenotypes to Advance Computational Translation at Yale) study, a large-scale, longitudinal research project in computational psychiatry. The project aims to advance the field by integrating mechanistically informed behavioral tasks with theory-driven mathematical modeling in a transdiagnostic clinical cohort of approximately 2,400 individuals. The research combines traditional clinical assessments, computational behavioral tasks, and spoken narrative data to characterize longitudinal trajectories of core computational constructs such as reward learning, decision-making, and cognitive control. The postdoctoral associate will lead and contribute to the computational analysis of behavioral task data, working closely with experts in computational psychiatry including Drs. Xiaosi Gu, Robb Rutledge, Sarah Yip, Chris Pittenger, and Godfrey Pearlson. Responsibilities include implementing and validating computational models of cognition and behavior (e.g., reinforcement learning, decision-making, belief updating), coordinating large databases for harmonized analyses, modeling individual differences and within-person change over time, and integrating theory-driven models with data-driven analytic approaches to improve prediction of symptom trajectories. The associate will also contribute to analytic strategy discussions, model comparison, interpretation in a clinical context, manuscript preparation, and presentation of findings at national and international conferences. Applicants must have a PhD in psychology, neuroscience, psychiatry, cognitive science, computational neuroscience, statistics, applied mathematics, or a related field. Essential skills include computational modeling of behavioral data, model fitting, parameter estimation, model comparison, and proficiency in scientific programming languages such as Python, MATLAB, or R. Preferred qualifications include experience with longitudinal or hierarchical modeling, familiarity with transdiagnostic approaches to psychopathology, interest in translational applications, experience with large-scale datasets, and a strong publication record. The position is full-time, with an initial one-year appointment and the possibility of renewal for up to three years based on performance. Salary and benefits are in accordance with NIH and Yale guidelines. The training environment offers mentorship in grant writing, leadership in collaborative projects, and opportunities for developing an independent research program in computational psychiatry. To apply, candidates should submit a cover letter, curriculum vitae, and contact information for 2–3 references to [email protected]. Applications are reviewed on a rolling basis until the position is filled. For more information, visit the provided links.