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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
: 277494287 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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associate 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
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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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group's efforts in modeling combustion-generated aerosols. These modeling framework will be used to understand the impact of inorganic aerosols on sunlight scattering and droplet/ice crystal nucleation
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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. The appointment
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of the problems are constrained by inherently low-quality or noisy data, and the successful candidate will be enthusiastic about contributing to data preprocessing and curation in addition to model development and
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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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or more senior research positions to analyze and model the delivery of clean energy and industrial decarbonization infrastructure associated with net-zero transitions. The role will report to the Andlinger
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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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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