Postdoctoral Scientist: Neuroimaging & Bayesian Methods

The University of Vermont; Burlington (VT); Hybrid; USD 63,000 - 77,000; Full time

Job summary

The University of Vermont is seeking a Postdoctoral Associate in the Department of Psychiatry to lead analyses of large longitudinal neuroimaging datasets and investigate developmental trajectories related to cannabis exposure. The role emphasizes methodological rigor, open science, and collaboration within a multidisciplinary team.

Ideal candidates will have a Ph.D. in a quantitative field, expertise in R and Python, and experience with Bayesian statistics, causal inference, and machine

Qualifications

• Postdoctoral degree in a quantitative, computational, neuroscientific, or closely related discipline.
• Hands-on experience analyzing longitudinal neuroimaging data from large multisite studies.
• Strong grounding in Bayesian statistics and causal inference frameworks (e.g., Bayesian networks, cross-lagged models, propensity score methods).
• Background in complex systems science and its application to neurobiological or psychiatric research.
• Advanced proficiency in R and Python for statistical computing.
• Experience applying machine learning methods within rigorous cross-validation frameworks.
• Experience with data management, quality control, and data sharing in large-scale, multi-wave studies.
• Peer-reviewed scholarly productivity with strong communication skills.

Skills

R programming

Python programming

Bayesian statistics

Causal inference

Machine learning

Education

Postdoctoral degree in quantitative, computational, or neuroscientific discipline

Tools

Neuroimaging data processing

Job description

The University of Vermont is seeking a Postdoctoral Associate in the Department of Psychiatry to lead analyses of large longitudinal neuroimaging datasets and investigate developmental trajectories related to cannabis exposure. The role emphasizes methodological rigor, open science, and collaboration within a multidisciplinary team.

Ideal candidates will have a Ph.D. in a quantitative field, expertise in R and Python, and experience with Bayesian statistics, causal inference, and machine