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Doctorate Degree in relevant scientific fields (including but not limited to genetics, genomics, computational biology, biomedical sciences, or evolutionary biology). Previous lab experience Preferred
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
have a strong research background in evolutionary genetics and/or computational genomics. Preferred Qualifications, Competencies, and Experience The ideal candidate will be an individual who is highly
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to have a Ph.D. or equivalent in evolutionary biology, genomics, or related fields. Candidate must have excellent computational and bioinformatic skills as well as strong experience with next-generation
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. The mission of the Srivatsan Lab is to develop sequencing tools to understand how biological systems are constructed and how these constraints shape subsequent evolutionary trajectories. Further
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Associate - Immunobiology Posting Number req26581 Department Immunobiology Department Website Link https://immunobiology.arizona.edu/ Location Tucson Campus Address 1656 E. Mabel Street, Tucson, AZ 85721 USA
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research grants. These goals include learning about demographic history or natural selection from genetic variation data, or understanding genetic architecture and evolutionary history of complex traits in
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systems. · Exploring the evolutionary diversity of Cas13 effectors and their implications for technologies. The successful candidate will receive interdisciplinary training in CRISPR-Cas systems, RNA
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. Eisenberg about also applying for the Biological Mechanisms of Healthy Aging Training Program (https://halo.dlmp.uw.edu/bmha/post-doctoral-openings/). The position is 100% FTE for 12 months. Benefits such as
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genetics, genomics, computational biology, biochemistry, machine learning, population genetics, or evolutionary biology). The lab is multi-disciplinary, and applicants from a variety of backgrounds
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and systemic health using an evolutionary health framework. There will be a specific emphasis on applying advanced bioinformatics and statistical approaches to large, population-level datasets in