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system models and quantifying uncertainty in their predictions. Conduct fundamental research on the formulation of probabilistic system dynamics models, including knowledge integration, likelihood
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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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in interdisciplinary fields, knowledge of codes in related disciplines, such as thermal hydraulics, actinide chemistry, fuel cycle analysis, particle physics, or uncertainty quantification. A minimum
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credible claims of quantum advantage. Develop and apply physics-informed AI/ML and digital-twin capabilities to improve modeling, parameter inference, uncertainty assessment, and adaptive feedback between
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-time data acquisition and telemetry systems Familiarity with cloud computing platforms and edge deployment of ML models Experience with uncertainty quantification, sensitivity analysis, or robust
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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
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to the development of scalable, explainable, and uncertainty-aware AI methods that enhance model robustness, reliability, and scientific discovery. Publish research findings in high-impact journals and present results
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) methods for modeling and optimization of metallic materials and advanced manufacturing processes. Participate in the design of integrated, scalable numerical methods and uncertainty quantification. Follow
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Gaussian-process emulators for accelerating parameter estimation and uncertainty propagation Selective cross-scale evaluation using complementary ecosystem observations (e.g., experiments) to test how AI