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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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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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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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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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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
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
nature makes them susceptible to complex damage modes like delamination, fibre breakage, and matrix cracking, especially under high-velocity impacts from projectiles or debris. Current assessment
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industrial SBSP experts, the candidate will explore the nonlinear structural dynamics of LSSs to fully understand the complexity of their control. They will use this foundation to explore idealised and
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habitat fragmentation. Working at the forefront of ecological modelling and movement ecology, you will build next-generation, process-based models to predict how real populations respond to complex