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proteomic/metabolomic data integration * Integration of clinical and multi-omic microbiome data. * Biologically informed data interpretation * Experience with Python, R and command line Ideal candidate: A
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. · Biologically informed data interpretation · Experience with Python, R and command line Ideal candidate: A computational biologist or bioinformatician with strong programming skills and a firm background in
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of clinical and multi-omic data to uncover microbiome–host relationships -Experience with Python/R and cloud computing required Ideal candidate: A computational biologist or bioinformatician with strong
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and development (R&D) of CADt, CADe, and CADx-type tools with strong translational potential for rapid detection/triage, injury grading, and quantitative visualization of hemorrhage burden. We
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scientific research involving human cell lines, mouse models, and clinical samples. Interested candidates should email CV and contact information for three references to Valeria R. Mas, MS, PhD, FAST Professor