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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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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty
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behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help of the simulation tool To support in the development and
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; from data integration to data-related topics such as uncertainty quantification, model-order reduction, or multi-fidelity methods. The primary fields of application are life science, medicine and health
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investigate online feature engineering, continual learning and uncertainty quantification. Balance performance with governance. Projects will evaluate risk-adjusted returns alongside interpretability
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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