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in AI, digital innovation and resilience together with strong analytical, communication, and independent research skills. Funding Sponsored by Cranfield School of Management, this studentship will
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design, technology and management expertise. We link fundamental materials research with manufacturing to develop novel technologies and improve the science base of manufacturing research. The Integrated
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establishing events and aiding future design improvements. Currently, there remains a paucity of data in this domain, making it difficult to identify any notable trends and associated failure mechanisms
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designing research approach and drawing on a wide range of social science methods. Key commercial sectors include (but are not limited to) data centres and high-tech industries, as well as food and beverage
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innovation and digital technologies to support decision making and strategic planning. This work will provide support and guidance to help airports develop actionable plans and identify opportunities
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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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, 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 public stakeholders to ensure
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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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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