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will combine digital twins based on established process designs and process engineering fundamentals with data-driven optimisation techniques, specifically Bayesian statistics and Bayesian optimisation
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theory, Bayesian inference, Monte Carlo simulation, and statistical analysis of subjective data. Data science and machine learning - big data analytics, surrogate modelling, digital twin development, and
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skills Biomedical Data Science UG degree: List A List B Introduction to Medical Ethics Python language programming Probability theory, statistics and Bayesian analysis Artificial intelligence in Medicine
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modelling. Bayesian fusion/inference methods will also be integrated for state estimation, uncertainty quantification, anomaly detection, remaining-life prediction and operational optimisation. Research aims
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simulation results with experimental data. This project will integrate advanced AI techniques, including machine learning for parameter optimisation (e.g., Bayesian optimisation, reinforcement learning), AI
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networks for real-time, adaptive diagnosis. b) Uncertainty in Dynamic Environments: Runtime uncertainties require sophisticated risk modeling; we will employ Bayesian deep learning and deep reinforcement
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. Bayesian networks and related machine-learning methods will be used to calculate cross-section probability density functions in a much faster way, enabling the combination of multiple probability
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receive training and skills in some of the following: meta-barcoding, stable isotope analysis, trophic-web analysis, Bayesian statistics, wet-lab experimentation – respirometry, fieldwork. Previous