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and curious Postdoctoral Fellow to develop and drive projects on retinal disease modeling and repair. OPTI is a part of Genentech Research and Early Development (gRED): scientists in gRED
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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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on developing and applying foundational AI models to investigate clinically relevant cancer vulnerabilities and their relationship with molecular context. Spanning functional genomics and large-scale clinical
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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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models, or related machine learning approaches. Background in cancer biology, cancer genomics, or clinical data analysis in oncology Preferably a strong publication record or impactful first author paper
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, why you are applying for the position, and why you would be a good fit for the Genentech postdoc program (400 words max). 2. Mentors: Identify three Genentech postdoc mentors from Research Biology or
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fit for the Genentech postdoc program (400 words max). 2. Mentors: identify three Genentech postdoc mentors with whom you are interested in working with and describe why (200 words max).https