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, computer science. Experience in data analysis using statistical software & machine learning (e.g., R, Python, SAS, or STATA). Experience working with large population dataset (e.g. EHR, claims data
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, computer science. Experience in data analysis using statistical software & machine learning (e.g., R, Python, SAS, or STATA). Experience working with large population dataset (e.g. EHR, claims data
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 5 hours ago
reason why Carolina is one of the leading public research institutions in the country. UNC -Chapel Hill offers postdocs comprehensive medical and vision coverage , paid leave, and benefits and
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competitive salaries, an array of benefits, an extensive support network, and above all, an enriching and highly collaborative working community that is deeply passionate about our vision for higher
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competitive salaries, an array of benefits, an extensive support network, and above all, an enriching and highly collaborative working community that is deeply passionate about our vision for higher
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Requisition Id 16562 Overview: The Analytics and AI Methods at Scale (AAIMS) group at the National Center of Computational Science (NCCS) at the Oak Ridge National Laboratory (ORNL) is seeking
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conduct cutting-edge research under the mentorship of a Principal Investigator, Dr. Shusen Pu, to support projects under the Center for Computational Intelligence. This position will collaborate on the
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Ridge Leadership Computing Facility (OLCF). This position resides in the Advanced Computing for Chemistry and Materials Group in the National Center for Computational Sciences (NCCS), Computing and
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., near-miss detection, predictive crash risk, multimodal exposure estimation). · Design, implement, and validate AI pipelines (computer vision, tracking, spatio-temporal modeling, edge learning
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precipitation patterns with hydrologic and hydraulic models to quantify potential future flood impacts in the study watersheds. The project is supported by the Department of Energy Resiliency Center