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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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perturbation technology, AI/ML model development, or advanced molecular biology Experience in single-cell omics, spatial transcriptomics, and/or high-content imaging data analysis Demonstrated record
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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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that combine the engineering and analytical principles of multiple scientific disciplines. We are seeking a talented postdoctoral fellow to join our team and advance our mission by contributing to the discovery
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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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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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-reviewed journal. Extensive hands-on experience in protein biochemistry and expertise in cryo-EM. Independent and driven to perform at a high level in a collaborative and fast-paced environment. Team player
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technologies (single cell, CRISPR, spatial) and microscopy are highly desired. Expertise in spatial-omic (Xenium) data analyses and interpretation is highly desired. Experience with new technology development is
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genomics datasets. Work closely with laboratory colleagues in the design of relevant experiments to develop and validate biological hypotheses. May also participate in method or technology development as a