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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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custom tools and end effectors for robotic assemblyGood knowledge of the CAD software Rhinoceros 3D, its plugin Grasshopper 3D, and its API RhinoCommonReal-world architecture and/or construction
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modeling. Positions may begin as soon as September 2025 . A Ph.D. in Mechanical and Aerospace Engineering, Civil and Environmental Engineering, Atmospheric and Oceanic Sciences, Geosciences, Computational
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The Andlinger Center for Energy and the Environment at Princeton University seeks applications for two interdisciplinary postdoctoral research or more senior research positions to analyze and model
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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 until excellent fits are found.The
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skillsExpertise in Generative AI: Strong background in machine learning, with specific experience in Large Language Models (LLMs), and Vision-Language Models (VLMs)Excellent programming skills (Python is required
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/neuropixel probes and electrical microstimulation to study attention and decision making networks in a behaving animal model together with parallel studies in humans. The project is part of a NIMH Silvio O
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, investigating (a) cumulative environmental impacts, (b) the use of census microdata for social vulnerability modeling, and (c) population and built environment exposure to climate hazards. The broad agenda
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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