Sort by
Refine Your Search
-
Category
-
Field
-
, 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
-
, 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
-
uncertainty quantification, global sensitivity analysis, parameter identifiability, surrogate modeling, or Bayesian model selection and comparison. Experience contributing to open-source scientific software
-
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
-
applications, including criticality safety, burnup credit, source term generation, and dose rate compliance. Experience with uncertainty quantification, sensitivity analysis, parametric studies, and the
-
-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
-
machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model development. Complete simulation verification, model validation, uncertainty quantification, and documentation
-
, thermal transport, or related experimental probes, including the effects of instrumental resolution, data reduction, and observable reconstruction. Experience with uncertainty quantification, covariance
-
) 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
-
, multidisciplinary team environment. Preferred Qualifications: Knowledge of uncertainty quantification methods and causal inference for complex environmental systems. Experience with large-scale Earth system