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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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on sunlight scattering and droplet/ice crystal nucleation.The researcher will interact with graduate students and postdoctoral researchers in the research group and in collaborating experimental research groups
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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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), multi-level modeling, and experimental study design. Expertise designing studies with parents, infants, and school-aged children is particularly desirable. The Postdoctoral Research Associate will work
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for Innovation in Engineering Education. The position is in the area of Human-Centered Artificial Intelligence Research and Design.The Keller Center, an interdisciplinary hub within the School of Engineering with
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data analysis (e.g., RI-CLPM, growth-curve analysis), multi-level modeling, and experimental study design. Expertise designing studies with parents, infants, and school-aged children is particularly
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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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the development and testing of new materials. The work will involve reactor design and setup with gas flow capability and process optimization. Qualified candidates should have a Ph.D. in chemistry, physics
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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