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, Computer Science, Machine Learning, Artificial Intelligence, Engineering, Mathematics, Operations Research, Economics, Finance, or a closely related subject. Preference will be given to candidates with strong
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and meteorological modeling. Experience running or maintaining operational/automated forecast systems. Experience applying machine learning methods to air quality or geophysical data (e.g., forecast
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 17 days ago
); and propagation of physics-traceable soil moisture uncertainty into precipitation estimates. Machine learning approaches are welcome as tools within this framework — for example, learning the space–time
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will join a research group working on longitudinal models of multiple chronic diseases across the life course. The postdoctoral researcher will contribute to building and evaluating machine learning
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Position Description Be You. Be Bold. Choose Duke. Postdoctoral Associate Human-AI Research Pipeline (HARP) | Duke University Department/Unit: Social Science Research Institute Anticipated Start
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. The postdoctoral researcher(s) will join an international research environment at Umeå University, including Stat4Reg (www.stat4reg.se ), which develops statistical and machine-learning methods for register data
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Posting Details Posting Details Job Title Postdoctoral Teaching Scholar - School of Information Science Requisition Number DU15126 Department Name 8M300:Center for Instructional Communication Work
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. Knowledge of survival analysis. Knowledge of machine learning methods. Other Requirements Application materials must include: Curriculum Vitae (CV) Statement of research interests Names of three professional
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, Machine Learning, Data Science, Computer Science, Electrical Engineering, Biomedical Engineering, or a related field. Experience with image analysis, computer programming using Python and R, and machine
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven