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coalitions for delivering reliable, low-carbon energy services. Collaborating closely with UK Power Networks, SSE Energy Solutions, and the University of East London, you will develop robust economic Model
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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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electrical machines. Funding is provided for three and a half years covering tuition fees, an enhanced rate stipend and research costs associated with the project. Early application is advised as the position
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Machine Learning-based diagnostics and prognostics digital twin system will be developed, aiming to provide fast and reliable predictions of the health of gas turbine engines. Non-confidential operational
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members of staff. Research in the Department is organised into six themes : Causality; Computational Statistics and Machine Learning; Economics, Finance and Business; Environmental Statistics; Probability
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from the globally renowned Power Electronics, Machines and Control (PEMC) Research Institute , University of Nottingham. The project will be supported by the state-of-the-art electric motor manufacturing
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verification methodology and corresponding toolchain to detect and mitigate such threats to CPS at the design time making the CPS resilient-by-design. Typically, CPS are modelled as hybrid systems, comprising
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. In this project, you’ll have the opportunity to be trained and become a proficient user of a range of advanced experimental techniques. For instance, you’ll learn how to use in-situ X-ray Computed
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a growing field, with many applications in biomedical devices, electronics, and autonomous machines. Actuators to drive these robots utilise electronic, chemical, pressure, magnetic, or thermal
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cells or the tumor microenvironment reduces tumor growth and extends survival in preclinical models, underscoring their potential as dual-function therapeutic targets. To address this, we aim to develop