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programming skills in Python/MATLAB, and an interest in digital twin technologies, cybersecurity and machine learning. Entry Requirements Acceptable first degree: Computer Science or related disciplines
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confined battery geometries. Advanced modelling—including computational fluid dynamics (CFD) and transient thermal analysis—is required to accurately capture heat flux distributions, temperature uniformity
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of short-axis MR image sequences. Training You will be based at the Vision Computing Lab within the School of Computing Sciences, which specializes in deep learning for medical image analysis and neural
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Acceptable first degree - Physics, Mathematics, Natural Sciences, Engineering, Computer Sciences. The standard minimum entry requirement is 2:1. Mode of study: Full-time Start date: 1st October 2026 Funding
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, and edge computing, AASs are evolving into smart, interconnected solutions for addressing the dynamic challenges of modern cities. This project investigates how to coordinate these multifunctional
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capabilities to match the real outcome as closely as possible. Entry Requirements Acceptable first degree - Computer Science, Engineering, Physics or Mathematics. The standard minimum entry requirement is 2:1
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targeted must be extremely potent and are often natural products that, on their own, are too toxic to be used. In this project, we will use synthetic chemistry, both in solution and on solid phase, to design
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on such jets — although their detailed properties remain unexplored. In this project, we will develop new computational methods to compute and characterize these novel wave solutions, with particular emphasis
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-Markovianity. For sufficiently Markovian systems, the photon-photon correlations can be computed using the quantum regression theorem together with a Lindblad equation for atomic ensembles or a HEOM model of a
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converting a widespread pollutant, nitrate, to a valuable fertiliser and chemical feedstock, ammonia. Harnessing sunlight as an abundant and sustainable source of energy to drive the upcycling of waste