SPAN Postdoctoral Associate

Position Details

Position Details

The University of Vermont (UVM) Larner College of Medicine invites applications for a Postdoctoral Associate position in the Department of Psychiatry, under the mentorship of Matthew

D. Albaugh, Ph.D. This position is supported by a NIDA-funded R01 grant and represents a unique opportunity to engage in methodologically rigorous, high-impact research at the intersection of developmental neuroscience, psychiatric epidemiology, and advanced computational data science. UVM is a Carnegie R1 research university with a sustained institutional commitment to transdisciplinary inquiry and a vibrant intellectual environment anchored by the Neuroscience, Behavior and Health Initiative.

Burlington, Vermont — situated on the shores of Lake Champlain between the Green Mountains and Adirondacks — consistently ranks among the most livable and progressive small cities in the United States.

The Postdoctoral Associate will lead cutting‑edge analyses leveraging three of the largest multimodal longitudinal neuroimaging datasets in existence — ABCD, IMAGEN, and ENIGMA — to delineate developmental windows of vulnerability to cannabis exposure, characterize longitudinal trajectories of brain and behavioral change, and rigorously assess causal relations among cannabis use, neurodevelopment, and psychiatric outcomes. The successful candidate will bring demonstrated expertise in Bayesian statistical methods and causal inference frameworks (e.g., Bayesian causal networks, cross-lagged panel models, propensity score matching, discordant twin designs), as well as a strong theoretical and applied grounding in complex systems science as it pertains to biological and neuropsychiatric data.

Hands‑on experience with very large, longitudinal, multisite neuroimaging datasets is essential, as is demonstrated proficiency in neuroimaging data processing and analysis pipelines. The Associate must possess advanced competency in both R and Python, with demonstrated experience applying machine learning methods — including regularized regression, ensemble methods, and supervised classification — within rigorous cross‑validation frameworks. Experience with data management, quality control, and data sharing in the context of large‑scale, multi‑wave studies is required.

Candidates must hold a doctoral degree in a quantitative, computational, or neuroscientific discipline, with a record of peer‑reviewed publication commensurate with career stage. The ideal applicant will combine deep methodological sophistication with intellectual curiosity, collaborative acumen, and a commitment to open, reproducible science. This position offers an exceptional training environment, close mentorship from productive and well‑funded faculty, and direct access to some of the most powerful datasets in developmental psychiatric neuroscience.

• Postdoctoral degree in a quantitative, computational, neuroscientific, or closely related discipline
• Demonstrated background and hands‑on experience in the analysis of longitudinal neuroimaging data (e.g., structural MRI, diffusion imaging) from large, multisite studies
• Strong theoretical and applied grounding in Bayesian statistical methods and causal inference frameworks (e.g., Bayesian causal networks, cross‑lagged panel models, propensity score methods, discordant twin designs)
• Demonstrated background in complex systems science and its application to neurobiological or psychiatric research
• Advanced proficiency in R and Python for statistical computing and data science applications
• Demonstrated experience applying machine learning methods…