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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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positions. Dr. Wei Peng's group (www.weipengenergy.com) focuses on modeling institutional and human dimensions of energy transition to identify realistic and robust decarbonization strategies and provide
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. A major focus will be on the identification of small molecules from mass spectrometry-based metabolomics data, in part based on generative AI models of chemical structures. The position is available
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earth system model data, with an emphasis on Seamless System for Prediction and EArth System Research (SPEAR) for seasonal to multidecadal prediction and projection. The project will emphasize elements
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, combines advanced system neuroscience and computational modeling techniques to study planning in rodents engaged in dynamic spatial foraging tasks. The successful candidate will develop computational models
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on the identification of small molecules from mass spectrometry-based metabolomics data, in part based on generative AI models of chemical structures. The position is available starting July 2025, and will remain open
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researchers working on an NIH funded project focused on developing new systems models to examine social and biological drivers of infection inequality. The overarching goal of this postdoctoral position is to
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of laminar/neuropixel probes and electrical microstimulation to study attention and decision making networks in a behaving animal model together with parallel studies in humans. The project is part of a NIMH
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earth system model data, with an emphasis on Seamless System for Prediction and EArth System Research (SPEAR) for seasonal to multidecadal prediction and projection. The project will emphasize elements