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software package VAMPIRE (vampire.york.ac.uk ). The successful candidate will benefit from strong national and international collaborations throughout the project and will develop skills in high-performance
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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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integrating machine learning, computational modelling, and experimental validation. The successful candidate will receive training in both computational and experimental biology within a highly collaborative
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include very high temperature stability, high mechanical stability and longevity, excellent chemical resistance, and tuneable microstructure. Combined with the design freedom of 3D printing, we have the
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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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-performance computing, and direct interactions with experts in AI for Science, computational imaging, and large-scale experimental methods (with Prof. Jeyan Thiyagalingam). You will spend time at both sites (75
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the development of a high k Si3N4 that retains its mechanical performance is very desirable and the subject of current research around the world. The highest k value yet achieved in a laboratory is ~170 Wm-1K-1
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Vision Laser processing is fundamental to advanced manufacturing, powering precision welding in battery manufacturing, high-performance turbine drilling, and cutting advanced materials for clean
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Ltd and Imperial. The project will develop high-performance lightweight alloys and composites for aerospace and other demanding engineering sectors, while improving manufacturing efficiency and
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and thermal models developed with an industry partner, the research will simulate coupled heat and fluid transport in sedimentary reservoirs and assess system performance under varying operational