2021 UCSF Postdoctoral Fellow


University of California, San Francisco


San Francisco , California

Principal Investigator

Michael Keiser


Interested candidates should submit a CV and arrange that three letters of reference be sent directly to Please reference “postdoc-dnn-ucsf-genentech-Alzforum.”


The Keiser Lab at UCSF in collaboration with the Discovery Chemistry group at Genentech is looking for highly motivated postdoctoral candidates with a background in machine learning, computational chemistry, chemical informatics, or related fields. The candidate would work to explore chemical space through the lens of machine-learning models. The project involves the design and testing of algorithms to map and quantify chemical latent space for use in drug discovery. The postdoc’s primary appointment would be at UCSF but he or she will be closely integrated with Genentech collaborators.


Python expertise required. PyTorch experience preferred. Desired, but not strictly required, skills include experience with pandas, sklearn, dask, slurm, and GPU clusters. Expertise with massive and/or distributed dataset analysis is a plus. Computational chemistry, drug discovery, medicinal chemistry, or demonstrably related domain expertise is also required.

A productive track record with at least one first-author publication is required. The lab seeks a driven individual who will hit the ground running, lead her/his research independently, and communicate frequently and clearly to the field and industry partners.