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the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density
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have the prospect to blur the edges between the exact and statistical approach, and the work of the PhD student proposed here would be the first study of this kind, with this kind of level-density
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the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density
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reduce pain. However, recent research has shown that even where there is no clear cause of the CLBP, people use their muscles differently compared to those without pain, presenting an opportunity
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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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of materials science, combining ab initio density-functional theory (DFT) calculations with novel ML methods. You will develop ML-assisted computational screening methods, building highly accurate models
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that can anticipate and adapt to the behaviour of humans and other autonomous agents. This PhD project aims to investigate the integration of Theory of Mind (ToM) reasoning into autonomous agents to improve
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unexpected directions and creating unique opportunities to uncover new fundamental physics. This PhD studentship offers the chance to develop an ambitious research project at the interface of theory and
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About the project: Developing a Theory of the Magnetic Fingerprint of Stress in Materials Supervisor: Dr Chris Patrick, University of Warwick In the development of sustainable materials and
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systems? In this project, we will partially address these questions and study prototype LC systems e.g., LCs inside shells, cylinders, cuboids etc. within the celebrated Landau-de Gennes theory for LCs