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Memorial Sloan Kettering Cancer Center | New York City, New York | United States | about 21 hours ago
Role Overview: We are seeking a highly motivated computational biology postdoctoral fellow to lead translational studies in single cell and spatial biology of gastric cancer immunology and evolution
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). Execute bioinformatic and statistical analyses on high-throughput single-cell and spatial transcriptomic datasets. Prepare high-quality manuscripts, assist with grant reporting, and present findings
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driven to make a biological impact. In particular, we are seeking a postdoc to take primary leadership for analyzing spatial and single-cell multiomics datasets collected from human tissue samples as
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/Python (spatial data processing, zonal statistics, raster/vector integration). ○ Experience or interest in conducting qualitative or mixed-methods stakeholder research (e.g., focus group facilitation
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options Employee and dependent educational benefits Life insurance coverage Employee discounts programs For detailed information on benefits and eligibility, please visit: http://uhr.rutgers.edu/benefits
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on KEAP1/NRF2 signaling in cancer and neurodegeneration; understudied kinases, their networks and functions; one-carbon metabolism; proximity proteomics; chemical screens and therapeutic discovery, spatially
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Associate I, Immunobiology Posting Number req26998 Department Immunobiology Department Website Link https://immunobiology.arizona.edu/ Location Tucson Campus Address 1501 N. Campbell Ave., Tucson, AZ 85719
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virus, chikungunya virus, SIV, and HIV. The lab also conducts functional genomics studies using high-throughput RNA sequencing (RNAseq) including ATCseq, CITEseq, bulk mRNAseq, and GeoMx spatial-seq
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datasets (bulk, single-cell, and spatial). Primary responsibilities include developing sequencing pipelines, maintaining project databases, designing validation experiments, and drafting manuscripts and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 20 days ago
, Competencies, and Experience Strong programming skills in Python and/or R. Experience analyzing high-dimensional biological data, especially single-cell sequencing data. Strong statistical and quantitative