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related discipline. This project would suit a candidate with a background in mechanical, control or aerospace engineering, physics, mathematics, or other relevant engineering/science degree. The ideal
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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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. The project is co-sponsored by Spirent Communications, a world leader in navigation and testing technology. Spirent will provide advanced simulation tools, expert support, and industry placements to help make
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Develop practical, industry-transforming technology in this hands-on PhD program focused on immediate industrial applications. This exclusive opportunity places you directly at the interface between
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is increasingly planned, managed, and engineered. New research is needed to understand how such areas, and particularly their dynamic nature, influence perceptions and decision-making. The studentship
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-efficient research that prevents fatigue failures has pushed towards integrated computational materials engineering approaches that improve competitiveness. These approaches rely on physics-based models
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
between advanced sensing and analysis, enabling fast, reliable, and quantitative damage assessment of impacted composites. This project lies at the intersection of composite materials engineering, impact
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physical wellbeing. We are committed to progressing the diversity and inclusion agenda, for example; gender diversity in Science, Technology, Engineering and Mathematics (STEM) through our Athena SWAN Bronze
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opportunities for high-impact dissemination at premier conferences (e.g., IEEE S&P, USENIX Security, NeurIPS). Furthermore, Cranfield’s strong industry links provide a direct pathway for technology transfer and
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands