11 uncertainty-quantification Postdoctoral positions at Oak Ridge National Laboratory
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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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uncertainty quantification, global sensitivity analysis, parameter identifiability, surrogate modeling, or Bayesian model selection and comparison. Experience contributing to open-source scientific software
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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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-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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, thermal transport, or related experimental probes, including the effects of instrumental resolution, data reduction, and observable reconstruction. Experience with uncertainty quantification, covariance
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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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, multidisciplinary team environment. Preferred Qualifications: Knowledge of uncertainty quantification methods and causal inference for complex environmental systems. Experience with large-scale Earth system
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of analytics into production systems Knowledge of experimental design, uncertainty quantification, scientific machine learning, or digital twin methodologies Experience collaborating across national laboratories