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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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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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applications to alternative fuel design and atmospheric chemistry. The successful candidate will be expected to assist with the commissioning of a new shock tube facility and will conduct fundamental
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applications to alternative fuel design and atmospheric chemistry. The successful candidate will be expected to assist with the commissioning of a new shock tube facility and will conduct fundamental
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September 2025. The Ferris group studies high-temperature reaction chemistry and particulate formation using optical diagnostic methods, with applications to alternative fuel design and atmospheric chemistry
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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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The School of Engineering and Applied Sciences at Princeton University seeks applications for a postdoctoral position at the Keller Center for Innovation in Engineering Education. The position is in
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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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of design, computation, and robotics. ARG's research interests include topics such as robot learning, human-robot interaction, Generative AI, computer vision, closed-loop control, extended reality (XR), and
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of individual assets that underpin most proposed energy transitions. These models will be used to design and test policy and investment interventions to alleviate deployment bottlenecks.The successful candidate