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Previous Job Job Title Post Doctoral Fellow, Lake Ecosystem Modeling Next Job Apply for Job Job ID 57582 Location Columbia Full/Part Time Full-Time Add to My Favorite Jobs Email this Job Job
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of such disturbances on ecosystems in order to develop tools that support the prevention of these natural risks. This interdisciplinary project, bridging lake ecology and mathematical modeling, is
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lakes - Good knowledge of mathematical and statistical modeling methods in ecology - Proficiency in optical microscopy and flow cytometry tools - Very good knowledge of algal community identification
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USNH Employees should apply within Workday through the Jobs Hub app The research position is within the broad area of biophysics, structural modeling, and molecular dynamics (MD) simulations
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molecular and cellular mechanisms governing centrosome function, cytoskeletal organization, and tissue morphogenesis. Using zebrafish and cell culture models, the lab integrates advanced live-cell imaging
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solutions and have the opporutnity to contribute to world-class research, we encourage you to apply! Opporuntities to Contribute Develops dynamic river-lake network models to track the geochemical and
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isotopic compositions (δ13C and δ18O) of ostracod valves from sediment cores collected in European lakes. To synthesize existing data, interpret them and promote them in the form of scientific publications
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-funded project that focuses on tracing the impact of atmospherically deposited per- and polyfluoroalkyl substances (PFAS) in the Great Lakes region. The project seeks to quantify PFAS deposition from
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 1 hour ago
USNH Employees should apply within Workday through the Jobs Hub app The research position(s) are within the broad area of biophysics, structural modeling, and molecular dynamics (MD) simulations
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, to define novel biomarkers, and to identify novel therapeutical targets. We have pioneered in the integration of genetics with omic data to identify proteomic signatures and develop novel predictive models