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the optimization-based methods (doi.org/10.1016/j.apenergy.2020.116152 ), 3- Weakness of the model-predictive-control (MPC) against HESS’s parameters uncertainties, noises, and disturbances (doi.org/10.2514/6.2022
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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model predictive control (MPC) methods to enable large groups of buildings to dynamically form coalitions and provide flexible energy services. Your work will incorporate advanced robust MPC techniques
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marginal structural models will be extended with machine learning techniques for counterfactual prediction and to support sensitivity analyses Candidate The studentship is suited to a candidate with a strong
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AI approaches have recently been used to detect Alzheimer’s disease from CFPs among those with established disease (in case-control studies), the use of such approaches to predict disease (i.e., in
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such as antenna arrays; and the modelling of advanced electromagnetic structures such as metasurfaces, which allow for unconventional control and manipulation of electromagnetic fields. Students who enjoy
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of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
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resulting properties. However, two significant challenges persist in this domain. First, the extrapolation of ML predictions beyond the range of existing data remains problematic, as models often struggle
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: Computational Modelling: Employing simulation tools (e.g., GEANT4, light transport) to explore novel metamaterial designs, predict performance, and optimise key parameters such as timing resolution, light yield
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recovery in critical applications, including aerospace, healthcare, and industrial automation. Research Focus Areas: Predictive Analytics for Fault Detection: Develop AI models that predict potential system