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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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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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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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advance regenerative medicine. For more information about the lab, please visit https://mesa-lab.org/. Projects will utilize in vivo mouse models, transcriptomic techniques, and advanced intravital imaging
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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