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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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nanoparticles (NPs) interact with plants is crucial for, firstly, better identifying their impact on plant growth, their toxicity, their potential transmission through the food chain, and secondly, evaluating
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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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The Project The project focuses on understanding and improving the stability of lipid nanoparticles (LNPs) for use in vaccines. By investigating molecular interactions and structural properties
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methods to accelerate the discovery of better catalysts that use less platinum and have improved long-term stability. By combining large-scale density functional theory with machine-learned interatomic
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PhD position in Condensed-Matter Theory of Magnetic Topology Faculty: Faculty of Science Department: Department of Physics Hours per week: 36 to 40 Application deadline: 8 May 2026 Apply now
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chemistry such as molecular dynamics, density-functional theory and multi-scale modelling. You will work under the supervision of Dr Xiuwen Zhou and Dr Joshua Carroll in the School of Chemistry and Physics