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This self-funded PhD research project aims to develop smart sensors based on low-frequency resonance accelerometers for condition monitoring of ultra-speed bearings. The developed smart sensors will
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
. This PhD project will tackle that challenge by developing intelligent methods that combine AI techniques such as language models that interpret technical text and knowledge graphs that map engineering
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This PhD at Cranfield University explores the development of resilient, AI-enabled electronic systems capable of detecting faults and autonomously recovering from failures in real time. The project
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of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
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response have led to targets for reducing overflow frequency, ecological harm, visual impacts, and protecting bathing water. This PhD project has been co-developed with four UK water companies and the
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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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research opportunity focuses on advancing large-scale additive manufacturing using metal wire as feedstock and electric arc as the heat source. The project aims to develop an innovative and efficient method
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) of high-value critical assets. Through this PhD research, algorithms and tools will be further improved and developed, validated and tested. It is expected that combining the domain knowledge and the
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tuition fees. This PhD project in the area of autonomy, navigation and artificial intelligence, aims to advance the development of intelligent and resilient navigation systems for autonomous transport
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to develop enhanced NbS strategies that target micropollutant removal and remain compatible with other ecological and environmental benefits. The aims of this project are therefore to 1) benchmark the long