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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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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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relevant field of science. Preferred Majors/Disciplines: Biology, Biochemistry, Biomedical Engineering, Biomedical Science, Cell Biology, Chemistry, Chemical Engineering, Data Science/Bioinformatics
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Interactions at Genentech. Specifically, we are interested in elucidating the mechanisms by which bacterial factors, outer membrane vesicles or biofilm interact with the host mucosal or barrier immunity
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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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cellular heterogeneity and identify genetic alterations in immune cell dysfunction. CRISPR Engineering: Develop CRISPR-engineered cell lines to model specific mutations and evaluate potential therapeutic
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& Development division (gRED). The successful candidate will work in a highly interdisciplinary environment at the intersection of the Computational Biology, Translational Medicine and Technology Innovation
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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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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
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the design of relevant experiments to develop and validate biological hypotheses. The applicant will also participate in method or technology development. Scientific insights resulting from this research