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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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performance simulation capabilities for gas turbine engines developed at Cranfield University as the starting point. Applications are invited for a PhD studentship in the Centre for Propulsion and Thermal Power
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
We are pleased to announce a self-funded PhD opportunity for Quantitative assessment of damage in composite materials due to high velocity impacts using AI techniques. Composite materials, such as
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at leading international conferences and publish in top-tier journals. The successful candidate will gain advanced expertise in multi-sensor fusion, signal processing, machine learning, and positioning
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This self-funded PhD opportunity sits at the intersection of several research domains: multi-modal positioning, navigation and timing (PNT) systems, AI-enhanced data analytics and signal processing
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research group, which leads pioneering work in multi-sensor navigation, signal processing, and system integrity for aerospace, defence, and autonomous systems. The research will deliver a comprehensive
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of the overall efficiency of the system. Their degradation behaviour in different fuels (hydrogen, ammonia or bio-fuels) is yet to be understood. This PhD project aims to investigate the effect
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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
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trust in digital communications and readily bypass conventional security controls. This PhD research proposes to design, develop, and validate a novel, explainable, multi-modal detection framework. By
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-periodic structures, we can precisely control the interaction of radiation with matter, potentially achieving unprecedented timing resolution (sub-70ps) and significantly enhancing signal detection. This PhD