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- Interfacing machine learning with climate models Company: Princeton University Description: The Atmospheric and Oceanic Sciences Program at Princeton University, in association with NOAA's Geophysical Fluid
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- Interfacing machine learning with climate models Company: Princeton University Description: The Atmospheric and Oceanic Sciences Program at Princeton University, in association with NOAA's Geophysical Fluid
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conduct research on the use of machine learning in ocean climate models. The goal is to demonstrate the successful use of machine learned parameterizations of unresolved processes that will reduce biases in
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approaches for mass spectrometry data, with artificial intelligence/machine learning (AI/ML) being a major focus. They will have an opportunity to lead and contribute to a range of exciting projects
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. Successful candidates will pursue research and may teach up to two courses per year, subject to sufficient enrollments and with the approval by the Dean of the Faculty. The initial appointment is for one year
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access to PLI's $9.6M GPU cluster (300 H100s), among the largest machine learning computer cluster in academia. In addition, PLI seed grants enable advances in AI-related research in the humanities, social
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the first line of defense, the intestinal epithelium must acquire nutrients from the diet while maintaining harmony with a vast consortium of microorganisms known collectively as the microbiota. Endemic
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to centennial changes in the statistics of tropical cyclones over the past millennium, recent historical era and the coming centuries, using a combination of high-resolution climate modeling, machine learning
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University's dedication to excellence. PI241328070 Create a Job Alert for Similar Jobs About Princeton University Princeton University is a vibrant community of scholarship and learning that stands in
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, biogeochemical models, and machine learning. The ideal candidate will have a background in ocean modeling, but candidates with the necessary background in ocean biogeochemistry and/or geophysical fluid dynamics