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compressible gas dynamics, heat transfer, free-surface/melt behaviour, and mass transfer driven by phase change, within a GPU-accelerated solver to reduce simulation turnaround times. You will develop and
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compressible gas dynamics, heat transfer, free-surface/melt behaviour, and mass transfer driven by phase change, within a GPU-accelerated solver to reduce simulation turnaround times. You will develop and
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with experimental virologists to validate computational predictions Impact and Outlook: This project will uncover the untapped structural and functional potential of bovine UL-CDRs, laying the groundwork
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the central challenge hindering this vision: the fundamental incompatibility between text-native LLMs and the operational reality of computer networks. Directly applying LLMs is impeded by three core technical
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A fully funded four-year PhD position is available to work on the project titled “Fault-Tolerant Architectures for Superconducting Qubit Quantum Computers”. This position is a collaborative
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computing and the use of GPU clusters. Entry Requirements Acceptable first degree - Computer Science/Physics/Maths The standard minimum entry requirement is 2:1. First class in bachelor degree or a master
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simulation workload and update the solver data structures when the mesh changes. These approaches would be applied on modern large-scale heterogeneous parallel computing environments where both CPUs and GPUs
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-class or 2:1 (or international equivalent) Master’s degree in Computer Science, Robotics, Mechatronics or Electronic/Electrical Engineering, or a related field. • Knowledge of machine learning/deep
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transformations at such interfaces, and how they are influenced by external electric fields and electrolyte composition. Access to high performance computing facilities including GPU clusters will be provided
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quality, diversity, and biological relevance using standard metrics and expert review. Anonymised digital images from tissues in biobanks will be used to train generative models on university computing