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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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millennium, recent historical era and the coming centuries, using a combination of high-resolution climate modeling, machine learning/artificial intelligence (ML/AI) techniques, theory and observational
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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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of climate change and resulting human responses. Successful applicants should be skilled in one of two areas of research: 1) modeling of physical characteristics of extreme events (particularly heat waves and
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of climate change on human mobility. Successful applicants should be skilled in econometric methods, social network analysis and other modeling approaches applied to assessment of human migration and other
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of stratospheric aerosols on global radiation, stratospheric chemistry and circulation, and global climate and extremes, principally using a hierarchy of climate modeling experiments. The Term of appointment is
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(BSPL). Individuals with a PhD and research interests in psychology, sociology, behavioral economics, management, or another social or behavioral science relevant to the study of human behavior on climate
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climate. The broad scope is improved representation of processes in models, high-resolution modeling, application of novel machine learning methods, and advancing the understanding of the Earth System
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work with Professor Jess Metcalf and Dr. Elena Shevliakova on research at the interface of plant pathogens and global climate change, observed and projected into the future. The successful candidate will
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applying this knowledge of decision making and motivations to climate change. There is also a special emphasis on cross-cultural studies. The BSPL seeks a researcher who will develop and deploy surveys in