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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 12 hours ago
traditional predictive attempts and limits the availability of training data for high-resolution atmospheric and hydrological models. This limitation is compounded by the fact that many atmospheric reanalysis
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26 Jan 2026 Job Information Organisation/Company CNRS Department NAnomédecine, Biologie extracellulaire, Intégratome et Innovations Research Field Pharmacological sciences Engineering Technology
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research in deep learning models for multi-sensor satellite data (e.g. SAR, SMAP) within a large international research project on AI-driven solutions for groundwater management. Expected start date and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
science leaders with the knowledge and skills to thrive in this data-driven world. The SDSS will serve the state, the nation, and the world through premier data science educational programs and innovative
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), Energy Science and Technology Directorate (ESTD), at Oak Ridge National Laboratory (ORNL). Major Duties/Responsibilities: Develop physics-based computational models, including Finite Element Analysis (FEA
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Postdoctoral Research Fellow with expertise in large language models (LLMs) and electronic phenotyping to join our dynamic team focused on advancing cancer research through innovative data-driven approaches in
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intersection of solid mechanics, constitutive theory, and data-driven modeling, while contributing to fundamental advances in soft material mechanics and developing transferable skills applicable to a broad
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Control (GAC) Group of the Power Systems and Engineering Center (PSEC) at NREL is looking for an exceptional Ph.D. graduate to support research in the modeling and simulation of the modern power grids
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curious to deliver work that matters, your journey starts here! Innovation. Interdisciplinary collaboration. Complex problem solving. In Carnegie Mellon University’s Department of Mechanical Engineering
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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative