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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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vulnerability modeling, and (c) population and built environment exposure to climate hazards. The broad agenda of this research is assessing the fitness of geospatial indicators to inform conceptual and policy
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interpret statistical or computational research, and follow developments in the fast-changing field of AI, including but not limited to, familiarity and technical understanding of AI models and their uses and
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(LLMs), AI systems, and text analysis; to write computer programs to manipulate information, devise programs for estimating econometric models; and to write summaries of research, and perform literature
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, to study novel renewable energy technologies. The candidates are expected to have a PhD degree in Chemical Engineering or related field, and have experience with optimization (theory, modeling, and tools
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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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debt capacity model. Produce management reports (both financial and graphic) to convey the status of the debt program and to support recommendations. Represent the needs of the department to both
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: 272540334 Position: Development and analysis of Global-Nest and Global Storm Resolving Models Description: The Atmospheric and Oceanic Sciences Program at Princeton University, in cooperation with NOAA's
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The Skinnider Lab at Princeton University aims to recruit a postdoctoral fellow or more senior researcher to work on projects related to computational analysis of chemical and biochemical datasets
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, combines advanced system neuroscience and computational modeling techniques to study planning in rodents engaged in dynamic spatial foraging tasks. The successful candidate will develop computational models