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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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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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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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on the scientific, technical, policy, and human dimensions of environmental issues. These areas include issues surrounding global change; energy and climate; biogeochemical cycles; molecular geochemistry
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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
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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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earth system model data, with an emphasis on Seamless System for Prediction and EArth System Research (SPEAR) for seasonal to multidecadal prediction and projection. The project will emphasize elements
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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