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areas: -developmental biology -experimental and/or theoretical biophysics -experimental and/or computational genomics -computer science, statistics, and/or machine learning with applications relevant
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to investigate questions related to cell adhesion or morphogenesis. Other essential qualifications are a experience with computational and statistical methods, a strong work ethic, the ability to work in an
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institutions, and has as its mission to discover, interpret and disseminate information about human cultures, the natural world and the universe through a wide-ranging program of scientific research, education
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well as a passion for quantitative-biology and synthetic-biology research. The ideal candidate should have a recent Ph.D. in microbiology, evolutionary biology, computer science, physics, applied mathematics
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 21 hours ago
background in microbial genomics, evolutionary biology, bioinformatics, or computational biology, with an interest in translational applications that connect bacterial evolution to clinical impact. Experience
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. The Department of Biology at Texas A&M University is responsible for research and teaching within the vast disciplines of the biological sciences, from molecular cell biology to ecology and evolutionary biology
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. Experience with comparative and evolutionary genomic approaches Relevant laboratory skills for comparative genomics (HMW DNA extraction, library preparation, cell culture). Experience with natural history
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Department Contact Email hli-byarlay@centralstate.edu Job Summary/Basic Function Agriculture Research Development Program (ARDP) in the College of Engineering, Science, Technology and Agriculture (CESTA
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new research project funded by the Florida Institute of Oceanography’s (FIO) Florida Restore Act Centers of Excellence Program (FLRACEP). The project is being conducted in collaboration with researchers
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level. The position requires working collaboratively with a team of experimental and computational biologists to integrate genetic and genomic data with mathematical modeling to better understand