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Field
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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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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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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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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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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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utilizing ex vivo airway epithelial cell models, including air-liquid interface (ALI) culture systems. Experience analyzing and interpreting transcriptomic datasets (e.g., RNA-seq). Experience working with
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, glial, and immune cell culture, nucleic acid extractions, ELISAs, bead-based microarrays, immunohistochemistry, spatial transcriptomics, and molecular analyses. The candidate will summarize research data
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; cloning; microdissection, especially of insect female reproductive organs; statistical analyses; single-cell RNA sequencing and transcriptome analysis; genetic mapping including QTL, GWAS, or PhyloG2P