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multiple objectives in real-time. The complexity of coordinating these distributed systems while ensuring stability and optimal performance presents a significant technical barrier that must be overcome
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techniques. This research proposes a novel framework that integrates Machine Learning (ML) for structural health monitoring (SHM) and design optimization of CFDST wind turbine towers. The study will focus
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—such as solar arrays, antennae, and habitat frameworks—while minimizing launch mass and deployment complexity. Key objectives include optimizing structural design for deployment efficiency, resilience under
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an interdisciplinary research environment at the University of Manchester, in preparation for a career in industry or academia. We are seeking highly motivated candidates with a strong academic background, holding a
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funded project between the University of Manchester and Croda International Plc. You must be a home student to be eligible to apply. The successful candidate will receive an annual tax free stipend set at
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methological design with advanced representation learning and transfer learning technologies, we will optimize the interface to handle individual variability and minimize environmental noise, thereby enhancing
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experimental and modeling approaches. Experimental investigations will be conducted at the University of Manchester, utilizing established rigs to simulate a spectrum of conditions, from single-phase heat