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for gene regulatory networks, single-cell multi-omics integration, spatial omics, and variant effect mapping in complex disease. Strong method/tool dev experience required (Python/R, ML/stats
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approaches including flow cytometry, spatial proteomics, transcriptomics, confocal microscopy and biochemical assays. Utilize mouse models and patient-derived samples to explore how genetic and dietary factors
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single cell, spatial transcriptomics of solid tumors. This has a variety of data, including cosmic, stereoseq, nanostring, 10X Visium, xenium and 10x single cell data. The ultimate goal is to find new
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or testing deep learning models for genomics, exploring new techniques related to spatial simulations, or other topics discussed with the PI. Core job duties include: (1) Designing, implementing, and