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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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) with questions related to this position. Major Duties/Responsibilities: Develop and apply machine learning models (ML) as surrogates for high-resolution process-based hydrologic models. Design and
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Postdoctoral Research Associate - Theory-in-the-loop of Autonomous Experiments for Materials-by-Desi
in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte
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environment? If so, the Oak Ridge National Laboratory’s Learning Systems Group within the Data and Artificial Intelligence Systems section invites you to apply to our new postdoctoral research associate
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toward integration of hydropower with battery storage and other technologies. Computational and analytical skills : Demonstrated ability in selecting and deploying machine learning tools (Random Forest
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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physics (HEP) detectors, neuromorphic computing, FPGA/ASIC design, and machine learning for edge processing. The successful candidate will work with a multi-institutional and multi-disciplinary team
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laboratory investigations with inorganic mercury and methylmercury. Acquire and analyze data using a range of analytical instrumentation. Maintain detailed and accurate records. Prepare oral and written
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
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of agentic AI for science, scientific reasoning, federated & collaborative learning, and reinforcement learning (RL) for self-improving models, in the context of leadership scientific workflows and