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will involve designing beam dynamics experiments, measurement, simulation, and data analysis. This position resides in the Accelerator Physics Group in the Accelerator Science and Technology Section
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simulation and flood inundation modeling. River basin planning and operations modeling, including reservoir simulation and optimization. Hydrodynamic modeling of water temperature and quality constituents
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analysis (FEA)—to evaluate and predict the behavior of complex rotating machinery operating under extreme conditions. Through the integration of modeling, simulation, and experimental validation, the group
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) at Oak Ridge National Laboratory (ORNL) is seeking a Research Associate to perform R&D in the areas of bulk power systems electromagnetic transient (EMT) simulations, high-fidelity dynamic and transient
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Requisition Id 15869 Overview: Oak Ridge National Laboratory (ORNL) is seeking a Research Professional in simulation area to directly contribute to materials development and manufacturing R&D, with
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candidate will bring a strong foundation in systems architecture, a working knowledge of cluster computing and scaling, and a passion for advancing the security of AI systems under real-world and simulated
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needs. By leveraging advanced simulations, machine learning, and data-driven insights, the group enables more effective operations aligned with evolving energy demands. The group also develops hydrologic
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implement hybrid approaches that integrate process-based simulations with data-driven methods to advance hydrologic process understanding and prediction. Integrate diverse datasets (e.g., in situ observations
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research spanning detector simulation, Spiking Neural Network (SNN) design, neuromorphic hardware, and data-rich experimental systems such as CMS pixel detectors, Timepix4, and novel photodetector
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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