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international collaborations with clinicians, regulators, policymakers, and industry partners. You must have a strong background in machine learning, computer vision, and medical image analysis, with publications
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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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interpretation of the data generated and that’s where this project comes in. You’ll be applying metagenomic techniques to respiratory samples and developing analysis and interpretation approaches that facilitate
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bottleneck in the screening process. This PhD project will address this through deep integration of scanning probe electrochemistry, optical microscopy and machine vision, to develop a system that can
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desorption mechanisms of various FFA on different type of metallic surfaces as a function of temperature and concentration. The modelling data and principal component analysis will be used to build property
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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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interpretation of the data generated and that’s where this project comes in. You’ll be applying metagenomic techniques to respiratory samples and developing analysis and interpretation approaches that facilitate
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design, thermal optimisation, and AI-guided system efficiency, while gaining transferable skills in data analysis, resource modelling, sustainability evaluation, and design automation. Exposure to both
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This PhD opportunity at Cranfield University invites candidates to explore the integration of AI into certification and lifecycle monitoring processes for safety-critical systems. The project delves
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, breaking etc. This makes traditional methods particularly difficult to generalise based on all these driving factors, whereas this PhD project takes a novel and explainable data-driven approach – equation