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a focus on meat and dairy promotion. In partnership with Adfree Cities, the student will investigate: (1) the link between advertising exposure and mental health using large-scale secondary data and
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This large-scale ecological project investigates the barriers and drivers of post-fire forest recovery. With climate change and the spread of forest fires to new areas, it is important to
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
predictive and explainable digital twins. The core challenge this PhD will tackle is how to help digital twins make sense of complex, messy maintenance data and turn it into clear, useful insights
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characteristics might explain these differences? MS is a complex disease, with damage to the brain and nervous system that varies widely between individuals. This PhD project offers an opportunity to investigate
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As a PhD student within the Economics Division, you will have access to a broad programme of research training opportunities. These include advanced methods courses, data skills workshops, and an
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In the process of laser shock peening (LSP), metallic structural components are hit by controlled laser pulses. The sudden vaporisation of material at the structural surface creates large magnitude
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made. The project will involve using routinely collected healthcare data and/or large healthcare datasets such as the Clinical Practice Research Datalink. This is an excellent opportunity for someone
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PhD Studentship: Revolutionising Litz Wire Development for Next Generation Ultra-High Speed Propulsion Motors The Manufacturing Technology Centre UK, and University of Nottingham This project offers
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PhD Studentship: Carbon Nanotube (CNT) Winding Development for Electric Motors The Manufacturing Technology Centre UK, and University of Nottingham This project offers an exciting opportunity
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One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour