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, intelligent monitoring systems and predictive technologies have become essential competitive advantages. This project sits at the intersection of data science, engineering, and design innovation, addressing
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at the edge. The project explores advanced topics such as TinyML, neuromorphic design, reconfigurable logic, and autonomous fault recovery, with applications ranging from aerospace, energy, and robotics
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sectors like aerospace, healthcare, and manufacturing. The convergence of AI with fault-tolerant design principles is transforming traditional maintenance paradigms, leading to more robust and intelligent
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-disciplinary approach that integrates design, technology and management expertise. We link fundamental materials research with manufacturing to develop novel technologies and improve the science base
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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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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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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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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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. 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