64 phd-data-mining Postdoctoral positions at University of Washington in United States
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. For more information, please visit the University of Washington Labor Relations website. Qualifications Minimum Qualifications * PhD awarded by the start date in a relevant field, such as library and
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conditions, and brain tissue microstructure and functioning. The successful candidate will be working within a multi-disciplinary team of MRI physicists, computer scientists, radiologists, neuroscientists, and
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from a range of stakeholders (e.g. NOAA, ICCAT, DFO). The PDRA will work within the Marine Ecology Research Lab, a large lab consisting of four faculty, two PDRAs, five PhD students, two Masters
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and staff, and providing support in applying for additional research grants. There is also opportunity to conduct analyses of already-collected data. This position has availability for onsite or remote
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criteria apply. For more information, please visit the University of Washington Labor Relations website . Candidates must hold a PhD or demonstrate completion of all degree requirements by the start of
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and staff, and providing support in applying for additional research grants. There is also opportunity to conduct analyses of already-collected data. Job Description Primary Duties & Responsibilities
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Description The Department of Earth and Spaces Sciences at the University of Washington seeks a Postdoctoral Scholar to work on numerical simulations and data analysis to inform the search for life
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Spaces Sciences at the University of Washington seeks a Postdoctoral Scholar to work on numerical simulations and data analysis to inform the search for life on exoplanets. The position will be supervised
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& Responsibilities: Information on being a postdoc at WashU in St. Louis can be found at https://postdoc.wustl.edu/prospective-postdocs-2/ . Trains under the supervision of a faculty mentor including (but not limited
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is 7/1/24. Qualifications Completed PhD or a foreign equivalent. The current project seeks to revolutionize the entire process from data to prediction with a paramount focus on reproducibility and