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hamster models. This is a chance to work at the frontier of infectious disease research â” using barcoded viruses to track exactly how pathogens spread within and between hosts, and to measure how novel
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of Virginia (UVA) School of Medicine is seeking to fill Postdoctoral Researcher positions in computational biology and machine learning. The lab develops modern machine learning, generative modeling, and
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modelling to identify environmental predictors of mould growth preparing scientific publications, conference presentations, and project reports for stakeholder audiences supporting ethics applications, data
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scientific manuscripts and other documents for publication. Fact-check, proofread, and edit research documents to ensure accuracy. Maintain the laboratory work areas including routine maintenance on laboratory
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assemblages (Perspektywiczna Cave) will be used to check the variability of the isotopic baselines in other ecosystems (mammoth steppe and tundra). We will further use the C-N flux model obtained for the modern
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, mammalian cell culture, transfection, gene silencing, lentiviral infection for gene silencing and overexpression, and animal studies using xenograft, syngeneic, and PDOX models assessing the effects of drugs
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University of California, Los Angeles | Los Angeles, California | United States | about 2 months ago
) defining imaging predictors of domain-specific cognitive, motor, and behavioral symptoms; and (3) developing a spatiotemporal model to map individualized disease progression. This is a prospective, multi
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scientific manuscripts and other documents for publication. Fact-check, proofread, and edit research documents to ensure accuracy. Maintain the laboratory work areas including routine maintenance on laboratory
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intracellular bacteria that cause emerging zoonoses. The candidates will utilize in vitro and in vivo experimental models (e.g. ticks and mice) to study novel Type IV secretion effectors and other bacterial
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boson decays, searches for supersymmetry and other new phenomena, and measurements of rare standard model processes. We vigorously pursue the use of machine learning techniques for data analysis