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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
ratio, corrosion resistance, and design flexibility. However, they are susceptible to complex internal damage under high-velocity impacts—such as delamination, fibre breakage, and matrix cracking—that is
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candidate would have experience with computational modelling and control of dynamical systems. Other useful skills include scientific programming (e.g., Python or Matlab), control system design, and
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shifts, and stringent latency demands render traditional beam management ineffective. This project will design, implement, and validate an AI-native predictive beam-steering framework that combines orbital
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cities, where benefits are unevenly distributed, and how design or management interventions could enhance resilience and equity. A key component of the research will be developing advanced spatial models
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. The developed new knowledge will assist performance designs, analysis, operations, and condition monitoring of sCO2 power generation systems. The project will be undertaken using the strong thermodynamic
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the implications for urban decision-making and resilience. The PhD researcher will have flexibility in the design and implementation of the project, adjusting the focus based on their interests and the latest
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Manufacturing is one of eight major themes at Cranfield University. The manufacturing capability is world leading and combines a multi-disciplinary approach that integrates design, technology and management
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. This project leverages and advances these trends, targeting the development of multi-functional coatings to enhance EV battery safety. This PhD will design and characterise intumescent, dielectric, lightweight
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the intersection of ecology, machine learning, and sustainable land management, the research will combine field data collection, deep learning model development, and stakeholder co-design to support biodiversity
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collaboratively within a dynamic team, utilising cutting-edge technology to create low-cost and user-friendly sensors for deployment by citizen scientists. The project will involve co-designing the sensors with