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This PhD project will focus on developing, evaluating, and demonstrating advanced data analytics solutions to a big data problem from aerospace or manufacturing system to uncover hidden patens
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performance degradations and unwarranted system failures can occur. There is certain physical information known a priori in such aerospace platform operations. The main research hypothesis to be tested in
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reduces crack propagation in composites, reduce failure due to delamination and significantly improves fracture toughness [Williams et al, Journal of Materials Science 48, 3, 1005-1013, 2013]. In addition
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second class UK honours degree or equivalent in a related discipline. This project would suit students with an aerospace or mechanical engineering background. Experience of computational fluid dynamics
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and controlling defects and lay the foundation for a thermal physics-based approach to process qualification. Additive manufacturing (AM) is a rapidly evolving technology that continues to drive
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evaluation. Prognostics is an essential part of condition-based maintenance (CBM), described as predicting the remaining useful life (RUL) of a system. It is also a key technology for an integrated vehicle
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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel
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systems safer, more efficient, and more sustainable. The aim of this project is to design a smart cognitive navigation framework that information from various sensors and learn to make decisions on its own
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the challenge of forever chemicals in drinking water. The aim of this research is to develop a smart data predictive model that will support utilities’ evidence-based decision-making to improve the resilience and
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years EligibilityUK, EU, Rest of world Reference numberSATM450 About the host University and Through-life Engineering Services (TES) Centre Cranfield is an exclusively postgraduate university that is a