The Keiser Lab at UCSF in collaboration with the Discovery Chemistry group at Genentech is looking for highly motivated postdoctoral fellow candidates with a background in machine learning, computational chemistry, chemical informatics, or related fields. The fellow 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 fellow’s primary appointment would be at UCSF but they will be closely integrated with Genentech collaborators.
- Python expertise required
- Computational chemistry, drug discovery, medicinal chemistry, or demonstrably related domain expertise is required
- PyTorch experience preferred
- Desired, but not strictly required, skills include experience with pandas, sklearn, dask, slurm, and GPU HPC clusters
- Expertise with massive and/or distributed dataset analysis is a plus.
A productive track record with at least one first-author publication is required. We seek 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.
Just north of Silicon Valley, the lab’s location at UCSF Mission Bay directly adjoins SoMa district and the heart of SF’s tech and artificial intelligence startup scene. Our collaborators at the nearby Genentech South San Francisco campus are committed to discover effective medicines for unmet medical needs through the application of state-of-the-art drug discovery technologies.
How to apply
Interested candidates should submit a CV and arrange that three letters of reference be sent directly to email@example.com. Please reference “postdoc-dnn-ucsf-genentech”.