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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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) organization which uses data, by developing best in class computational methods, and applying them to the most relevant scientific problems across all stages of the pipeline. This position is based within
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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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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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teams. The Opportunity: Opportunity to work closely with computational colleagues to analyze, evaluate, and perform integrative computational analyses. Leverage multimodal high dimensional data to explore
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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