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. Simulations are suitable to characterise processes in healthy and diseased individuals including stroke patients. Machine learning methods might be considered to accelerate simulations. The project provides a
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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
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of diverse industrial processes. While mono-material felts offer simplified recyclability, blended fibre felts remain essential for applications requiring enhanced performance, such as high
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to the Net Zero targets. In consultation with the wider CDT community, the work will also include the development of a roadmap for the maturation of the technology and the processes required to have it adopted
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to scrap-based EAF steelmaking, by using a high percentage of scrap supplemented with ore based metallics (OBMs), is an attractive route to decarbonise the steelmaking process. However, residual elements
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techniques and advanced sampling methods to bring a significant advancement in reducing high-fidelity runs to accelerate the engineering design, validation process and improve the robustness of the prediction
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from the Defence Science and Technology Laboratory (Dstl). Award value The Studentship covers: Stipend: at UKRI rate + London Weighting (£22,780 for 2025/26 academic year) Tuition fees: UK Home Fees for
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machine learning (applied to spatiotemporal data). International and UK applicants are both eligible to apply. Sponsor: This scholarship is funded by the UK Engineering and Physical Sciences Research
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conceptual and experimental framework for engineering functional nanostructures from bottom-up principles. The work will benefit from access to robotic processing platforms and state-of-the-art microscopy and
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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel