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                Develop practical, industry-transforming technology in this hands-on PhD program focused on immediate industrial applications. This exclusive opportunity places you directly at the interface between 
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                Engineering, Faculty of Engineering and Applied Sciences, Cranfield University, in the area of performance simulation, analysis, and optimization of supercritical CO2 power generation systems. Cranfield has 
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                AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhDbetween advanced sensing and analysis, enabling fast, reliable, and quantitative damage assessment of impacted composites. This project lies at the intersection of composite materials engineering, impact 
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                Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel 
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                capable of leveraging signals from terrestrial base stations, non-terrestrial networks such as LEO satellite, and complementary on-board sensors. Specifically, it will: To design reconfigurable airborne 
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                directly addresses one of the most pressing challenges in modern information security: the malicious use of synthetic media. The relevance is acute, as deepfakes and AI-powered phishing campaigns are being 
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                research centres. It is anticipated that the new materials’ understanding and the model generated within the project will enable a faster uptake of AM materials for rotating components and help 
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                significant weight savings, and improve the overall efficiency of the Hydrogen system, and at the end, of the aircraft. Nevertheless, to deliver those expected benefits, it is absolutely necessary to understand 
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                year. Working at the intersection of water engineering, environmental microbiology, robotics, and lifecycle analysis, you will evaluate autonomous underwater skimming robots that minimise energy use in 
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                models such as Random Forest and Neural Networks to help understand and predict pairwise interactions between pollinators and plant species. - Software Engineering: integrate models into a standalone