Postdoc position on ML-driven analysis of spatial gene expression profiles in mice

The Abbasi Lab at the University of California, San Francisco (UCSF) invites applications for two fully-funded postdoctoral positions at the intersection of machine learning and gene expression analysis. This project aims to provide a computational framework to reveal building blocks of spatial gene expression profiles in the mouse brain. These building blocks or principal patterns will parcellate the brain into completely data-driven 3D brain areas and establish brain-area-specific local gene networks. Additionally, the project aims to leverage these analyses to better understand the patterns in the human gene expression datasets. The project involves designing and validating interpretable machine learning frameworks and statistical tools. This is an opportunity to work on a high-impact interdisciplinary problem in collaboration with the Allen Institute and UC Berkeley.

We are looking for highly-motivated individuals with a strong quantitative background (PhD in computer science, computational biology, engineering, computational neuroscience, or related fields) and experience in programming and data analysis. Previous background in computational biology and neuroscience could be highly beneficial.

UCSF is part of the 10-campus University of California, the world’s premier public research university system, and the only of its campuses dedicated to graduate and professional education. UCSF Neurology has been consistently ranked among the top departments of neurology at US medical schools. The successful candidate will benefit from various clinical and basic science resources at UCSF including extensive learning and research support. As a part of the Weill Institute for Neuroscience, our laboratory is located on the Mission Bay campus providing access to the UCSF Neuroscience and Bioengineering communities including the Neuroscape, Kavli Institute for Fundamental Neuroscience and Baker Computational Health Science Institute.

Additional information is available at: ​

Candidates should send their CV, research statement, expected date of availability, and the contact information for three references to