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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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more of the following areas: scientific machine learning, reduced-order modeling, surrogate modeling, system identification, equation discovery, uncertainty quantification, active learning
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detection and monitoring, invariant and distributionally robust representation learning algorithms, and deployment-time calibration with uncertainty quantification using approaches such as conformal
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-echelon and multi-location inventory systems; demand forecasting and uncertainty quantification; supply-chain planning, replenishment and allocation; stochastic and robust optimization under uncertain
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, agentic multi-modal data science for complex transport phenomena, in addition to forward/backward uncertainty quantification, numerical analysis, and high-performance computing. Equal Employment
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applications such as prediction, surrogate modelling, uncertainty quantification, and decision support. Coordinate the joint research activities between UESL and IMOS. Publish and present research
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group develops theory, methods and software for data-driven science, with a current focus on uncertainty quantification in large pre-trained models (vision-language models), generative models (flow
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uncertainty quantification (UQ) and propagation methods into ICME approaches. Interface with experimentalists in design of experiment and material development campaigns. Work both independently and
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skills and the ability to work effectively in a collaborative, multidisciplinary team environment. Preferred Qualifications: Knowledge of uncertainty quantification methods and causal inference for