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assigned to the research project "Randomization of Surrogates for the Quantification of Domain Uncertainty Propagation in Cardiovascular Models" as part of the Berlin Mathematics Research Center MATH+. The
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environment. • Excellent written and oral communication skills. Desirable Knowledge, Skills, and/or Abilities • Familiarity with multi-physics modeling frameworks. • Experience with uncertainty quantification
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uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments. • Collaborate with mathematical modelers and experimentalists in
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uncertainty quantification. Machine learning will be applied to identify when, where, and why forecasts can be considered forecasts-of-opportunity. This position seeks candidates with a background in
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motivated researcher with a strong background in computational modeling, system identification, and uncertainty quantification for civil infrastructure. The successful candidate will join the Risk Assessment
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tools. However, training opportunities (e.g., via NCAS) are available for motivated candidates. Interest in probabilistic methods, ensemble simulations, or uncertainty quantification; experience is
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models; 2. Statistical methods, analysis, and inference for large-scale computational simulator applications; 3. Uncertainty representation, quantification and propagation; and 4. Scalable data science
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modelling with remote sensing data, novel approaches to uncertainty quantification, especially as applied to environmental problems. In The University of Sydney’s Faculty of Engineering, you’ll join a group
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from telescope data. The design of robust uncertainty quantification tools is a core component of this effort. -On the experiment design side, the group develops simulation and optimization algorithms
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 2 months ago
and/or issues using discretion; experience with tritium transport modelling, hydrogen in materials, or fusion blanket concepts; familiarity with data assimilation, uncertainty quantification, or large