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: Computational Modelling: Employing simulation tools (e.g., GEANT4, light transport) to explore novel metamaterial designs, predict performance, and optimise key parameters such as timing resolution, light yield
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
to cutting-edge facilities including High-velocity impact testing, Advanced composite manufacturing labs, X-ray computed tomography and High-performance computing resources for AI model training This project
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/or increase in efficiency. Additive manufacturing (AM) could help increase the efficiency of the GTs by enabling complex designs. AM has been used for static GT components, however the use for high
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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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connect with nature. Their configuration, connectivity and interaction with surrounding land cover determine the extent to which they buffer heat, dilute pollution, support biodiversity and deliver social
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. The studentship is funded by the Leverhulme Trust through the Connected Waters Leverhulme Doctoral Programme. Urban blue networks, including rivers, canals and wetlands, are dynamic systems that shape how cities
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BASF, you will gain insight into ecological risk assessment, landscape-scale modelling and regulatory contexts. Cranfield University offers an advanced modelling environment, high-performance computing
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to ensuring safe, reliable, and high-performance communications. The development of 6G based AI networks with integrated TN and NTN infrastructures provides new opportunities for UAV tracking
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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