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significantly reduce the amount of vibration data to be stored on edge devices or sent to the clouds. Hence, this project's results will have a high impact on reducing the hardware installation and operation
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for global industries, all while collaborating with experts in the prestigious International Systems Realisation Partnership. Furthermore, the student will gain invaluable skills in data analysis, problem
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degree or equivalent in a related discipline. This project would suit individuals with academic or industrial experience in electronics, electrical engineering, systems engineering, or AI/data analytics
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Rolls-Royce the project will focus on the development and testing of novel ultrasonic methods to measure intake massflow for aero-engines. This technology has the potential to improve the methods
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Technology Centre (DARTeC), leading advancements in aircraft electrification, autonomous systems, and secure intelligent hardware. Through collaborations with the Aerospace Integration Research Centre (AIRC
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engineering, digital technologies, and systems thinking. The university’s strong reputation for applied research and its focus on technological innovation ensure that this project will be well-supported, with
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that facilitate seamless integration between AI hardware components and embedded systems, ensuring efficient data flow and processing. Cranfield University offers a distinctive research environment renowned for its
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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