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This PhD opportunity at Cranfield University explores how next-generation AI models can be embedded within resource-constrained electronic systems to enable intelligent, real-time performance
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This PhD opportunity at Cranfield University invites candidates to pioneer research in embedding AI into electronic hardware to enhance security and trustworthiness in safety-critical systems
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This PhD opportunity at Cranfield University invites candidates to explore the integration of AI into certification and lifecycle monitoring processes for safety-critical systems. The project delves
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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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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
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there be interest, there is also the possibility of developing teaching and supervision skills on our MSc Astronautics and Space Engineering programme. Sponsored by EPSRC and Cranfield University, this DLA
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
Fully funded PhD at Cranfield University, supported by the EPSRC DTP and Rolls-Royce. This 3-year project covers tuition fees, a tax-free stipend, and funding for training, conferences, and a
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Cranfield University and Magdrive, offer a fully funded PhD position under the umbrella of the R2T2 consortium to study the optimisation of their thruster for a kick stage. R2T2 is a UKSA-funded
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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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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