Postdoctoral Fellow/ Research Scientist in Computational Neuroscience
Thursday 17th April 2025
Contact Email for the Job Positing ai4mentalhealth@stanford.edu
Organization Stanford University
Location Stanford USA
Title Postdoctoral Fellow/ Research Scientist in Computational Neuroscience
URL https://cnslab.stanford.edu
Closing date Jul 31, 2025
Description Several Postdoctoral Fellow and Research Scientist positions in advancing discovery of MRI based phenotypes for improving personalized medicine and prevention of neuropsychiatric disorders are available immediately at the Computational Neuroscience Lab (http://cnslab.stanford.edu; PI: Kilian Pohl). The successful candidate will contribute to research and development in the rigorous multi-modal analysis and generation of longitudinal, multi-center brain MR images. The research projects are partially funded by the to the AI for Mental Health Initiative (https://ai4mh.stanford.edu), NIH, and other collaboration resources. The researcher will work in a multidisciplinary research environment closely collaborating and publishing with imaging, computer, and neuroscientists at Stanford University.
We are seeking highly motivated individuals who have demonstrated academic excellence, including publications in first-class journals and conferences. Successful candidates should have a PhD (or equivalent) in Computer Science, Neuroscience, or related fields. The applicant can either be an expert in machine learning, deep learning (especially diffusion models), and statistical methods for medical image analysis, or an individual with a strong background in computational or system neuroscience.
The Computational Neuroimage and Neuroscience Lab (http://cnslab.stanford.edu; PI: Kilian Pohl) is part of the Stanford initiative AI for Mental Health (https://ai4mh.stanford.edu), which aims to transform Psychiatry by replacing subject observations with object assessment resulting in more effective and accessible research and care. The CNS Lab performs multidisciplinary research focusing on developing deep learning technology to identify biomedical phenotypes specific to substance abuse, depression, sleep, HIV, and adolescent brain.