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Field
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cell immunology, mucosal immunology, and cancer immunology using animal models and single-cell multiomics/spatial transcriptomics approaches. This postdoctoral researcher will combine wet-lab molecular
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conditions by applying omics technologies (e.g., genomics, transcriptomics), validation of gene expression using quantitative reverse transcriptase polymerase chain reaction, and bioinformatic analyses
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fibrosis, in vitro and in vivo testing therapeutic candidates, scRNAseq, spatial transcriptomics, confocal and electron microscopy imaging, flow cytometry, and standard biochemical/molecular approaches. Our
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of muscle hypertrophic growth; 2) identifying the glucose transporter(s) activated by resistance training in muscle; and 3) examining the connections between glucometabolic flux and the muscle transcriptome
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of high-throughput imaging and molecular data (i.e., genome, transcriptome, epigenome, and more). The methods would be able to systematically integrate biomedical/biological knowledge to improve
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genomics data sets on the genome, epigenome, and transcriptome in disease-relevant tissues/cells and use of computational approaches to integrate and analyze this data to identify the molecular components
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of muscle hypertrophic growth; 2) identifying the glucose transporter(s) activated by resistance training in muscle; and 3) examining the connections between glucometabolic flux and the muscle transcriptome
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genetic basis of plant–microbe interactions, with a particular emphasis on data integration across plant species and data types (genomics, transcriptomics). Design, adapt and use deep learning methods
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cancer progression in the West lab in the Department of Pathology at Stanford. Successful candidates will use a combination of spatial transcriptomics and highly multiplexed imaging to understand how
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controllers, spatial transcriptomics profilers, PCR machines and flow cytometers. About You You will hold a PhD in immunology or related area with post-qualification research experience. You will have