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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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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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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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commitment to interdisciplinary research are especially encouraged to apply. Responsibilities will include: - Developing a computational Artificial Intelligence form finding design framework to shape
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, thermal management, and energy conversion. We seek candidates with strong expertise in building and conducting ultrafast time-resolved optical experiments. Key skills include the ability to design, assemble
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senior researcher in the areas of soft materials and polymer physics. The successful candidate will develop strategies to design, synthesize, and characterize the properties of soft materials using
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: Responsibilities *Explore, collect, and preprocess various sources to develop domain LLM training and test datasets *Design and implement fine tuning and RAG workflows for LLMs on a variety of datasets *Maintain
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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