30 parallel-computing-numerical-methods research jobs at University of Texas at Austin in United States
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background in hydrology, numerical/analytical modeling, programming, and data management/analytics is needed to support existing and forthcoming projects in the BEG hydro group. Purpose We are seeking highly
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researcher with a strong background in hydrology, numerical/analytical modeling, programming, and data management/analytics is needed to support existing and forthcoming projects in the BEG hydro group
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background in hydrology, numerical/analytical modeling, programming, and data management/analytics is needed to support existing and forthcoming projects in the BEG hydro group. Purpose We are seeking highly
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. Travis Mallard (Massachusetts General Hospital & Harvard Medical School). Both are interdisciplinary projects integrating theory and methods from developmental psychology and quantitative genomics
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of sedimentology, geochemistry, and microbiology, and general use of destructive, extraction-based methods that are designed for the analysis of large sediment samples. These bulk methods are quantitative but
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Postdoctoral Fellow position focused on developing advanced, AI-enabled methods for clinical decision support in precision oncology. The fellow will work at the intersection of computational innovation
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a cluster hire across two research areas: (1) Environmental Social Science, Education and Communication/Meaning-Making and (2) Environmental Data Science and Spatial Computing. This job listing is for
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defined benefit retirement plan, with 8.25% employer matching funds Additional Voluntary Retirement Programs: Tax Sheltered Annuity 403(b) and a Deferred Compensation program 457(b) Flexible spending
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. Experience with the use of electronic medical records and clinical data. Self-direction, critical thinking skills, and ability to work as part of a multidisciplinary team. High computer proficiency, including
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This position is in person at J.J. Pickle Research Campus in North Austin. Purpose To use advanced geochemical modelling to devise attribution monitoring methods for CO2 leakage in groundwater and surface