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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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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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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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, 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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optimization (theory, modeling, and tools). Candidates should apply at: https://www.princeton.edu/acad-positions/position/39361 and include a cover letter, CV (including a list of publications), research
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programs for estimating econometric models, and create and manage an active internet site are important aspects of this position. In addition, the successful candidate should be able to write summaries
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vitro and in vivo including analysis of mouse model of obesity and cancer. This is a one-year appointment with the possibility of extension. Depending on the prior experience of the applicant, candidates
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The Pena lab primarily studies the molecular mechanisms of brain development and the impact of stress across the lifespan, using a mouse model. This position will support the lab generally through
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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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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