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models to predict TCR-peptide/MHC (pMHC) interactions. Utilizing structural computational biology techniques to characterize and model TCR-pMHC interactions. Designing experiments to test and validate
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at Genentech (gCS). The group develops and applies state-of-the-art ML/AI models to address open questions in genome biology, with the goal of understanding causal mechanisms of disease and facilitating drug
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with multi-disciplinary teams of computational and laboratory biologists Preferred Qualifications: Background in cancer genomics and pre- and/or post-training of foundation models for biology is preferred
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omics, label-free microscopy, in vivo screens, and foundational models. The ideal candidate for this role has a strong foundation in both experimental and computational sciences, but exceptional
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apply cutting-edge machine learning algorithms, with focus on foundation models and LLMs/agents, to analyze complex biological data. This data includes gsingle cell genomics profiles, spatial data, and
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access to state-of-the-art analytics and a robust cross-functional scientific community. The Opportunity: You will be part of the Genentech post-doctoral training program that provides an outstanding
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below. Benefits Relocation benefits are available for this job posting. #LI-PL1 #postdoc Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat
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will be part of the Genentech post-doctoral training program that provides an outstanding environment and the opportunity to pursue fundamental basic science questions in an environment focused
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qualifies for the benefits detailed at the link provided below. Benefits Relocation benefits are available for this job posting. #LI-PL1 #postdoc Genentech is an equal opportunity employer. It is our policy
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provided below. Benefits Relocation benefits are available for this job posting. #LI-PL1 #postdoc Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise