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of mathematics, physics, electrical engineering and AI, helping to develop a theory that explains how and why these systems work — and how to design better ones. Why apply for this PhD? Work on the next-generation
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A Unified Framework for Reservoir Computing: From Theory to Real-World Systems Location: Faculty of Science and Faculty of Engineering, University of Nottingham, UK Start Date: 1 October 2026 This
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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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learning, control theory, and embodied autonomous systems. The successful candidate will contribute to the development of learning-based control methods that are not only high-performing, but also safe
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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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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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optimisation theory or an interest in the energy and sustainability implications of AI would be particularly welcome. Supervisors: Prof. Mark van Rossum (School of Psychology), Dr. Shreyank Narayana Gowda
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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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. 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
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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