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Award summary This studentship provides an annual living allowance (stipend) of £21,470, and full tuition fees (Home fee level only). Overview This project will develop uncertainty quantification
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that GHG fluxes will be interpreted in conjunction with subsurface hydrogeophysical data. Overall, the project's results will improve quantification and reduce uncertainties of the GHG budgets for the boreal
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Engineering, and Engineering Management. Students with interests in computational mechanics, optimization design, bioinspired design, sustainability management, machine learning, AI, uncertainty quantification
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modeling & environmental risk assessment. Numerical simulation techniques for hydrogeological systems. Advanced uncertainty quantification for robust modeling. Scientific communication, including
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recent large-scale capabilities in physics. Reliability, exploring uncertainty quantification and robust inference in machine learning. Explainability, leveraging identifiability and unique recovery
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) and deep reinforcement learning models Developing novel statistical models for uncertainty quantification, causality estimation, and prediction accuracy Publishing research in leading biomedical and
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on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in
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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty
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uncertainty quantification for robust structural design, particularly for complex aero-engine systems with limited experimental data. Recent work by the University of Southampton developed a novel data driven
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machine learning-based electrochemical-thermomechanical (ECTM) model capturing effects of material anisotropy on cell swelling across the scales with uncertainty quantification Experimentally-validated