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, including SuperMagdrive, operates using metallic propellant, offering density, integration, and safety advantages compared to conventional launcher propellants such as hydrazine. However, metal propellants
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models offer a powerful means to understand stroke mechanisms, predict treatment outcomes, and personalize patient care. By integrating numerical techniques like the finite element method and machine
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integrating Machine Learning (ML) with physics-based degradation modelling will enhance early fault detection, reducing unplanned downtime. This PhD is hosted at Cranfield University, a global leader in
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significant relevance in today's technological landscape. As industries continue to integrate digital and physical systems, the role of eCPS in enhancing automation, control, and sustainability becomes
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This is a fully funded PhD (fees and bursary) in experimental icing research. Fundamental understanding of droplet impact dynamics is integral to icing. The overall aim of this PhD is to use optical
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
will enable earlier fault detection, better understanding of system degradation, and more informed maintenance planning. Designed for scalability and resilience, the approach will integrate with existing
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part of the EPSRC Centre for Doctoral Training (CDT) in Net Zero Aviation, offering an integrated, multidisciplinary training programme focused on innovation, collaboration, and inclusive leadership. As
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with the strategic interests in developing ultra-efficient turbofans along with advancing propulsion integration and aerodynamic technologies. This project will help to de-risking the design of coupled
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development timescales and cost. This could yield efficiency improvements in areas like integrated fan-intake systems, very high bypass ratio engines, ultra-efficient boundary layer ingestion architectures
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this research is that it should be possible to significantly improve the performance of extreme learning and assure safe and reliable maintenance operation by integrating this prior knowledge into the learning