41 high-performance-quantum-computing "https:" "Simons Foundation" PhD positions at Cranfield University
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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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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-electronic systems, ensuring secure communication and operation. Side-Channel Attack Mitigation: Implement techniques to protect systems against side-channel attacks, safeguarding sensitive
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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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the computational inefficiencies of physics-based models and enabling faster, potentially more accurate predictions. However, AI models require substantial volumes of high-quality, labelled training data, which
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for Security Operations Centres (SOCs) while pioneering strategies for quantum-era resilience. This project sits at the intersection of Artificial Intelligence, Cybersecurity, and Explainable Computing. It
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intelligent systems aim to optimize power usage without compromising performance, employing strategies like power-aware computing and thermal-aware optimization. These systems are crucial in extending
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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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supported by the Enhanced Composite and Structures Centre at Cranfield. About the sponsor We will work in collaboration with Cambridge Nanosystems, which is a world leading high quality, high performance
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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