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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
: 271598471 Position: Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning Description: The Atmospheric and Oceanic Sciences Program at Princeton University, in
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
to develop hybrid models for sea ice that combine coupled climate models and machine learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation
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on the scientific, technical, policy, and human dimensions of environmental issues. These areas include issues surrounding global change; energy and climate; biogeochemical cycles; molecular geochemistry
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Geochemistry, Geomicrobiology, Environmental Chemistry, Biogeochemical Cycles, Paleoclimatology, Oceanography, Atmospheric Science, Geodynamics, Geochronology, Earth History, Seismology, and Planetary Science
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; biogeochemical cycles; molecular geochemistry; biodiversity; conservation; environmental science and policy; infectious disease and global health; and sustainable development in impoverished and resource
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Geochemistry, Geomicrobiology, Environmental Chemistry, Biogeochemical Cycles, Paleoclimatology, Oceanography, Atmospheric Science, Geodynamics, Geochronology, Earth History, Seismology, and Planetary Science
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; biogeochemical cycles; molecular geochemistry; biodiversity; conservation; environmental science and policy; infectious disease and global health; and sustainable development in impoverished and resource
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, Biogeochemical Cycles, Paleoclimatology, Oceanography, Atmospheric Science, Geodynamics, Geochronology, Earth History, Seismology, and Planetary Science. Postdoctoral Associate positions are available for one year
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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vulnerability modeling, and (c) population and built environment exposure to climate hazards. The broad agenda of this research is assessing the fitness of geospatial indicators to inform conceptual and policy