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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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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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. This work will involve: i) contributing to design and run new Large Eddy Simulation experiments; ii) and analyzing the LES output to generate training data; iii) using Machine Learning techniques to learn
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The Rosen Research Group at Princeton University (https://rosen.cbe.princeton.edu) is searching for a postdoctoral or more senior researcher interested in computational materials design and
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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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year with the possibility of renewal pending satisfactory performance and continued funding. About Us ARG is an interdisciplinary laboratory for advanced research at the intersection of design
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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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efficacy of such efforts. Responsibilities will include activities such as working with large public datasets, designing and implementing relevant experiments writing manuscripts, presenting research, and
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of reinforcement learning in the brain and leverage these models to assist the experimental groups with experimental design and multimodal data analysis of neurophysiological, causal, and behavioral data