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strengthen your application: Knowledge in kernel-based learning and uncertainty quantification is a plus. What you will do Perform research Publish in peer-reviewed international journals and conferences
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on either uncertainty quantification or how uncertainty should be expressed to users. https://unit.aist.go.jp/deihrd07/keiyaku_koubo/2025-airc_0043.html [Work content and job description] ・Develop uncertainty
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 5 hours ago
model benchmarking, uncertainty quantification, value of information analyses and delivery of modeling results to guide critical applications and drive innovation and prioritization in Earth Observation
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PhD project involves interdisciplinary research at the interface of computer science and mathematics and addresses a complex, coupled inverse problem with explicit uncertainty quantification. Research
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The Department of Mechanical Engineering at the University of Texas at Dallas is seeking highly qualified candidates for a Postdoctoral Researcher position in uncertainty quantification in experimental
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–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
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This project targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification
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-based analysis of structures - Experience with Monte Carlo simulation, FORM/SORM, or similar methods - Ability to work with experimental, monitoring, or inspection data for model updating and uncertainty
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statistics. We are looking for a motivated candidate, with a deep interest in mathematical statistics, with a view towards developing new methods for uncertainty quantification. Starting date no later than