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Despite significant advances in numerical techniques and computing hardware, the high computational cost of large-scale 3D computational fluid dynamics (CFD) modelling remains a major challenge. A
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and large models, limiting real-world deployment. This PhD focuses on efficient Physical AI, emphasising data-efficient training, reinforcement learning, continual adaptation and edge deployment
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This PhD asks a different question: instead of demanding more data, can we build language models that learn smarter from less? You will design AI architectures that adapt to the structure of a
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currently unavailable to clinicians. To unravel the hidden information, the student will apply advanced computational approaches to large-scale clinical datasets collected during VT ablation procedures
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profiles, language proficiency and accent familiarity Exploring how AI, speech models and large language models can support prediction of comprehension and listening effort Developing validated models and
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. Experience of working with large multimodal datasets. Interest in human-computer interaction and human-centred system design. Strong communication and organisational skills. While it is not necessary to have
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that extract material properties or defect information from measurements. You will work across the full research pipeline: running large-scale ultrasound simulations to generate rich training datasets, designing
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growth during the peak epoch of galaxy evolution, 3-9 billion years after the Big Bang. Using far-infrared spectrophotometry, PRIMA will simultaneously measure star formation rates, black hole accretion
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whole exome/genome sequence data in both clinical genetic studies and/or large-scale population studies, such as the UK Biobank Study. Applicants must have a degree in genetic epidemiology, statistical
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. Experience of working with large multimodal datasets. Interest in human-computer interaction and human-centred system design. Strong communication and organisational skills. While is not necessary to have