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-free stipend only (set by the UKVI, £20,780 for 2025/26). Killer waves or extreme waves are large (> 20 meters tall) and unpredictable surface waves that can be extremely dangerous to ships and other
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want to contribute to the next level of molecular computing? Are you excited about the application of AI tools to train molecular systems how to process information? Then join our team as a PhD candidate
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
intelligent reasoning and feedback mechanisms into digital twin environments, enabling them to interpret complex maintenance data more effectively. Using AI techniques, such as large language models, knowledge
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universities, and three research and technology organizations. This PhD project is focused on glass cutting, where large, laminated glass panels are cut to sizes for window production. The process involves pre
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are seeking a highly motivated PhD candidate to develop efficient on-device generative AI systems based on large language models (LLMs). The project focuses on creating compact, low-latency, and energy
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comfortable processing and analyzing large data sets in Python; experience with C++ (or another low-level language) is a plus Strategic thinkers with demonstrated interests in strategic games and/or competitive
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comfortable processing and analyzing large data sets in Python; experience with C++ (or another low-level language) is a plus Strategic thinkers with demonstrated interests in strategic games and/or competitive
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excellent logical reasoning and a passion for turning data into decisions Clear communicators in a fast-paced and highly collaborative environment Programmers comfortable processing and analyzing large data
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and colleagues located across the 4 nations of the UK. HDR UK’s mission is to unite the UK’s health data to enable discoveries that improve people’s lives. Its 20-year vision is for large-scale data and
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. They therefore contain critical information on the nature of the volcanic system, immediately prior to hazardous explosive events. Understanding them will generate critical information on the transition between