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, and remote-sensing data) to support model benchmarking, parameterization, and uncertainty quantification. Explore and apply AI/ML approaches (e.g., machine-learning emulators, surrogate modeling, AI
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, and remote-sensing data) to support model benchmarking, parameterization, and uncertainty quantification. Explore and apply AI/ML approaches (e.g., machine-learning emulators, surrogate modeling, AI
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through data-driven modeling and optimization. The successful candidate will work at the intersection of thermal-fluid sciences, control theory, and artificial intelligence/machine learning to advance
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Landscape analysis and modeling, with a focus on training and application of predictive machine learning tools. This researcher will synthesize regional-scale remote sensing imagery and ecological monitoring
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. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral
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, artificial intelligence, or machine and reinforcement learning applications for power systems. Experience contributing to successful research proposals and sponsored R&D programs. Demonstrated ability
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data processing and multigroup cross-section generation tools such as AMPX or NJOY. Experience applying artificial intelligence, machine learning, or surrogate modeling methods to nuclear engineering or
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(ORNL) is seeking a highly motivated Postdoctoral Researcher with expertise in artificial intelligence and machine learning (AI/ML), remote sensing, Earth and environmental sciences, and the analysis
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generating fusion energy. This research will focus on the chemical speciation and transport of tritium in the molten salt blankets using ab initio quantum simulations, machine learning potentials, and
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machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model development. Complete simulation verification, model validation, uncertainty quantification, and documentation