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promising tools for addressing these challenges. Large language models can help bridge communication gaps between subject experts, while knowledge graphs can capture complex semantic relationships and provide
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functional theory. In collaboration with Phasecraft, a leading quantum algorithms company, this project will explore the generation of new quantum computing datasets and the development of machine learning
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, computer science, electrical/electronic engineering, mechanical engineering, aerospace engineering, applied mathematics). Strong interest in control theory, reinforcement learning, robotics, or autonomous systems
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foundation in either machine learning or mathematical/computational neuroscience, demonstrable programming experience (Python/PyTorch), and the curiosity to work across disciplinary boundaries. A background in
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functional theory. In collaboration with Phasecraft, a leading quantum algorithms company, this project will explore the generation of new quantum computing datasets and the development of machine learning
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Centre of Excellence. This is a unique opportunity to work on advanced image analysis and image-driven modelling as part of a wider multi-disciplinary programme that includes mathematical modelling, cancer
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unique opportunity to work on advanced image analysis and image-driven modelling as part of a wider multi-disciplinary programme that includes mathematical modelling, cancer metabolomics and novel
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leverage advanced bespoke continuum robotic systems to demonstrate the feasibility of applying the proposed coatings can be deployed in-situ. Ultimately, this work bridges the gap between the theory
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in developing new theories of understanding by exploring research cutting across the following areas: Investigating material evolution and degradation mechanisms under cryogenic cyclical conditions
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. By integrating local probe measurements with complementary theory and materials growth efforts, the project will also strengthen critical links throughout the School of Physics and Astronomy, while