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Aviation by 2050. This exciting doctoral project, in collaboration with Rolls-Royce, will develop innovative computer vision methods which when combined with optical flow velocimetry will enable imaging
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
placement with Rolls-Royce. The research focuses on AI-driven digital twins, using large language models and knowledge graphs for predictive maintenance in aerospace systems. Aerospace systems generate vast
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reliant on efficient fluid dynamics. Furthermore, the novel nature of the findings presents an opportunity for patenting innovative porous airfoil designs, potentially leading to commercialisations driving
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, ultimately optimising the deposition process. Additive manufacturing (AM) is a rapidly advancing technology, driving numerous innovations and finding diverse applications across industries such as aerospace
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for improved operation of water treatment systems. The successful candidate will benefit from being part of a cohort of 12 students on the EPSRC Centre for Doctoral Training in Water Infrastructure and
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areas. Cranfield is part of the national testbed for 6G, researching in the following areas of interest: Real-time specification of 6G telecommunication and edge computing services using Large Language
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partners, (Boeing, BAE Systems, Rolls-Royce, Meggitt, Thales, MOD and Alstom); and from EPSRC. The investment, over the first 5 years of operation, was approaching £10M. We are now in our eighth year of
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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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automotive and motorsport courses at master’s level, and engaging in academic research and commercial consultancy. Interests are wide-ranging from mechanics and materials through to computer vision and self
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novel methodology for designing and assessing highly integrated fuel systems for ultra-efficient propulsion systems fuelled by SAF. The assessment will be done in terms of performance, operation and