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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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Biophysical and Biomedical Measurement Group - NIST | Gaithersburg, Maryland | United States | 22 days ago
processing, internal standards/calibration, and uncertainty quantification. - Experience with spectroscopy, chromatography, electrophoresis, fluorescence methods, or complementary molecular characterization
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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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, transfer learning, uncertainty quantification, multitask learning, or learning from sparse and expensive scientific data. Familiarity with atomistic or molecular-simulation software and interfaces, such as
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. This position is designed for scientists with strong computational and quantitative training who are interested in agent-based modeling, network science, infectious disease dynamics, uncertainty quantification
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Qualifications Experience with graph neural networks, machine-learning interatomic potentials, or related scientific machine-learning methods for atomistic systems. Familiarity with uncertainty quantification
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and machine-learning methods for rapid surrogate modeling, model calibration, state estimation, uncertainty quantification, and physics-informed prediction. Contribute to U.S. Department of Energy
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to SRC kinematics. Develop, test, document, and maintain scientific software for modeling, global fitting, uncertainty quantification, and data interpretation. Explore applications of the developed
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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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to high-dimensional statistics; Bayesian statistics; resampling techniques; digital twins; uncertainty quantification; foundations of machine learning and artificial intelligence; optimization theory and