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pollution, support biodiversity and deliver social value. Yet these trade-offs remain poorly quantified in complex urban landscapes. This PhD will investigate how urban blue networks can be optimised for both
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knowledge co-evolution and addressing complex challenges in a super-intelligent society. This project is situated within the rapidly evolving field of Cyber-Physical-Social Systems (CPSS), which is of
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drone detection and localisation performance using radar systems. This can be achieved by improving the detection performance of individual sensors and by employing a cooperative network of sensors which
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of representative failure models for gear failures causes difficulties in their useful lifetime prediction. Critical operational parameters such as loading, speed and lubrication affect the physics of gear meshing
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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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on multimodal human trust estimation, trust-adaptive decision-making, or cognitive human–machine interfaces that enhance safety and performance in complex environments. This project offers a unique opportunity to
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will publish high-quality research papers and present their work at international conferences to build global networks with leaders in academia and industry. We invite students interested in joining
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categories for a better capability of managing the uncertainty related to system complexity and data availability to achieve more accurate RUL estimations The student will have the opportunity to work with
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The South and East Network for Social Sciences (SENSS) Doctoral Training Partnership (DTP) is a consortium of 8 leading universities (of which Cranfield University is one) across the south and east
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value. Yet these trade-offs remain poorly quantified in complex urban landscapes. This PhD will investigate how urban blue networks can be optimised for both ecological resilience and community wellbeing