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prediction. In this thesis, you will focus on the quantification of model uncertainty, that arises from limited knowledge and the quantification of data uncertainty, that arises from data-inherent
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optimization, Bayesian inference, uncertainty quantification, or machine learning methods applied to engineering problems Ability to analyze and interpret experimental and simulation data, prepare peer
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that learn constitutive parameters from a single informative test through Bayesian optimization, together with quantification of measurement and model-form uncertainty. Approximately half of the effort
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features. No cookies to display. Reject Save My Preferences Accept Skip to content PhD student in Machine Learning with a focus on mathematical and statistical methods for uncertainty quantification
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resolved air-pollutant emissions and exposures from observational data. -Work may include statistical or Bayesian inverse modeling, data assimilation, optimization, uncertainty quantification, numerical
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of nuclear fuel behaviour in reactors, with particular emphasis on statistical methods, uncertainty quantification and machine learning to improve the accuracy and computational efficiency of fuel
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University of North Carolina at Chapel Hill | North Wilkesboro, North Carolina | United States | about 9 hours ago
of interest include, but are not limited to, robust and stochastic optimization, generative and foundation models, reliable and robust machine learning, uncertainty quantification, sequential decision
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 4 hours ago
of interest include, but are not limited to, robust and stochastic optimization, generative and foundation models, reliable and robust machine learning, uncertainty quantification, sequential decision
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Eindhoven University of Technology (TU/e) | Eindhoven, Provincie Noord-Brabant | Netherlands | about 2 months ago
? No Offer Description Are you fascinated by application-oriented research in mathematics and eager to work at the interface of numerical optimization, optical design, and uncertainty quantification
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more of the following areas: scientific machine learning, reduced-order modeling, surrogate modeling, system identification, equation discovery, uncertainty quantification, active learning