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computational colleagues to build, train, and evaluate cutting edge AI models using large proprietary oncology datasets Leverage multimodal high dimensional data to investigate relationship between heterogeneous
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. Scientific insights resulting from this research are expected to be presented at scientific conferences and published in high-impact journals. The Opportunity: Generate new methods for large-scale spatial
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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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the molecular pathways of disease and to establish the mechanism of action of our therapeutics. The successful candidate will work with our team to analyze multi-level biomarker data generated from pre-clinical
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synthetic chemistry and reaction optimization. A working knowledge of molecular biology and/or protein generation. For information about the (lab) at Genentech and publications, please go to: https
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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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author paper published or accepted in a well-recognized peer-reviewed journal More information about Man-Wah Tan’s Lab and recent publications: https://www.gene.com/scientists/our-scientists/man-wah-tan
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mammalian cell culture is preferred. Experience with or strong interest in learning computational structural biology approaches or cryo-ET is a plus. For information about the Deshpande Lab at Genentech