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methodology, theory, and applications across the areas of Bayesian experimental design, active learning, probabilistic deep learning, and related topics. The £1.23M project is funded by the UKRI Horizon
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outstanding research and publication record in the area of climate change and child health in low- and middle-income countries. They will be an expert in advanced quantitative methods including causal inference
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involve developing methods for complex trait analysis, scalable Bayesian and deep learning approaches, or algorithms for inferring and analysing large-scale graph data structures. Experience in statistical
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with the possibility of renewal. This project addresses the high computational and energy costs of Large Language Models (LLMs) by developing more efficient training and inference methods, particularly
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rendering into medical imaging workflows. A major focus will be on accelerating inference and training using GPU-optimised components, including custom CUDA kernels. This role offers a unique opportunity to
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inference attacks, to mitigate privacy leaks in MMFM. You will hold a PhD/DPhil (or be near completion) in a relevant discipline such as computer science, data science, statistics or mathematics; expertise in
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disrupt planning and inference in schizophrenia-related genetic mouse lines. Experiments will involve recording and manipulating prefrontal cortex and hippocampus activity in mice performing a newly
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at developing principled and practical methods which could be used in real systems. This research requires coping with challenges such as intractable probabilistic inference and robustness. The project will
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available data and apply causal inference methods, including Mendelian randomisation, to identify candidate mechanisms linking circadian misalignment and sleep disturbances with cardiometabolic disease
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analyses of viral proteins using AI-based prediction tools will also be integrated to support evolutionary inference. This work lies at the intersection of evolutionary biology, bioinformatics, and data