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emissions, and enhance occupant health and wellbeing. As a Research Assistant, you will work closely with UK- and Egypt-based teams to analyse collected data, develop and test computer-based retrofit models
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researcher with expertise in communication, project management, and leadership. You will build a robust national and international network and acquire advanced knowledge essential for implementing critical
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November 2025 or as soon as possible thereafter. This PhD project aims to explore how emerging datasets could provide value to the UK’s insurance industry through a combination of data analytics, modelling
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together world-class expertise in textiles, materials, soft robotics, biomechanics, sports, healthcare, machine learning and AI, with globally leading industrial and academic partners. Your Project
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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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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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: 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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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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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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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