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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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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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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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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
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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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. Armed with this information, the post holder will use cutting-edge paleoclimatic modelling that incorporates nutrient cycling and carbon chemistry (HadOCC) to infer the distribution of potential feeding
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. This can involve IoT connected devices, physical sensors or other instruments, including non-intrusive methods and inferences from a variety of data sources. You should have some experience with experimental
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applicant should hold or be near the completion of doctoral studies into hypersonic flows or numerical methods or hold equivalent professional experience. The ideal applicant should have a clear track record
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, microscopy, flow cytometry and quantitative real-time PCR. You will have a strong track record of writing papers as evidenced by publications or submitted manuscripts in scientific journals, and have evidence
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a track record of contributing to publications and presentations and an ability to contribute ideas for new research projects. Experience in experimental geophysical fluid dynamics, proficiency in