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Biology, Bioinformatics, Computer Science, or a related field • Strong programming skills in R and/or Python • Experience with analysis of single-cell sequencing data • Familiarity with spatial
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. Required Qualifications: A doctoral degree (PhD, MD, or equivalent) conferred by the start date. Proficiency in R/Python Experience with scRNAseq, and/or spatial proteomic/transcriptomic data analysis Growth
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tumor spatial biology via single cell spatial analysis of the tumor microenvironment. Projects aim to reveal molecular mechanisms of drug resistance based on intercellular and intracellular regulation
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microenvironment architecture that drives cancer progression and therapeutic resistance. The successful candidates will perform spatial omics profiling and analysis to infer biological and clinical insights and
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the Department and wider Stanford community to establish new techniques and research directions. • Apply and obtain competitive funding. • Employ advanced imaging, spatial analysis, and sequencing-based
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(Spiegel et al. Nat Med 2021, Majzner et al. Nature 2022, Frank et al. Lancet 2024). We have generated a wealth of single-cell sequencing, spatial, clinical, and other types of data from these and other
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our team. We are looking for postdoc candidates to develop and apply cutting-edge technologies in spatial transcriptomics, single-cell sequencing, machine learning, and functional genomics
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transmission modeling, statistical modeling, spatial data analysis, and cost-effectiveness analysis. In parallel, we conduct research on vaccine-preventable infections, developing and evaluating predictive
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outstanding resource to apply biomedical statistical tools for data analysis for our groups' ongoing preclinical work and tissue assays. Perform RNA sequencing, including bulk sequencing, single-cell sequencing