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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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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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to fossil fuels. In most sectors, battery power is at least part of the replacement. In the automotive sector, many countries are phasing out internal combustion engines entirely in favour of battery
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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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at least one programming language (ideally python). Experience in medical data processing is advantageous. Knowledge of CI/CD practices (e.g., git), containers (docker, singularity, or similar) and
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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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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
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operation of autonomous systems in complex, real-world conditions. This PhD project aims to develop resilient Position, Navigation and Timing (PNT) systems for autonomous transport, addressing a critical