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structural biology to tackle challenging scientific questions. Your responsibilities will include, but are not limited to: Multi-omics analysis of bulk and single-cell sequencing data. Developing deep learning
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, Chemistry, Chemical Engineering, Data Science/Bioinformatics, Microbiology, Pharmaceutical Sciences, Pharmacology, Toxicology or other related disciplines. Graduate student applicants should be within 12
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groups to study cancer biology using deep clinicogenomics data and cutting edge NGS diagnostics. The Opportunity: Opportunity to drive scientific, analytic, and technical innovation in a community of
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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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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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) 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