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sustainable engineering strategies to boost their performance to deliver benefits for the environment and society. The outputs of this work will support practitioners in making informed investment decisions
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Are you passionate about artificial intelligence, data analytics, and cutting-edge engineering applications? Join our interdisciplinary research team and contribute to the future of Non-Destructive
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been collected in Malawi under a joint agreement with the Government of Malawi, with field phenotype data, genomic sequencing data obtained in the UK, and micro-CT scanning data being also collected
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factors (learning curve). These will be linked with the CCTA imaging biomarker data (quantitative plaque analysis) that is being collected as part of the EU HORIZON TARGET study to determine personalised
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for cardiovascular applications. You will build on our group’s expertise on Physics-Informed Machine Learning (PIML), a powerful approach that combines data-driven AI with the rigour of physical and physiological
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PhD Studentship: Sleep and Circadian Rhythms in Elite Sport (Co-funded by Brighton & Hove Albion FC)
at BHAFC and a supportive supervisory team with expertise in sleep, sports medicine, sports science, and data analytics. The PhD researcher will design, conduct, and analyse data and review literature
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generations of research and development professionals, data specialists, technology experts, inventors, and scientists for industry and society. The Macroscopic Quantum Optics (MQO) Group at the Department
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Closing Date: 15 August 2025 Eligibility: UK Only Funding: Joint School of Civil Engineering/EPSRC Doctoral Landscape Award Studentship, providing the award of full academic fees, together with a
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properties of cirrus, but we are currently limited by a lack of observational data. This project will fill that gap, using existing aircraft and satellite measurements to determine whether aircraft exhaust is
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
complex engineering data and deliver insights that are robust, adaptable, and applicable across complex, high-value, safety-critical domains. This research will contribute to shaping the next generation of