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operational efficiency. Led by Professor Chris Holmes, the centre will initially focus on the following thematic areas: Decision analysis under model misspecification Uncertainty quantification around LLMs
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statistically reliable AI, with applications to engineering. The research will focus on the design of formal reliability guarantees for black-box AI models operating under highly non-deterministic and context
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partial drainage effects. You will contribute to the numerical modelling part of the project, which will benefit from novel element level and centrifuge testing experimental results. You will set up and
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-solution fit project in collaboration with QM Innovation and external consultants; R&D work on technical workflows and automation for remote sensing approaches to facilitate scaling of nature-based solutions
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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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disease. Your research may utilise a range of approaches, including targeted genetic murine models, primary cell culture and analysis, multi-omics, and bioinformatics. The biological focus will be
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the human microbiome with proficiency in laboratory-based immunology techniques, such as flow cytometry and ELISA. You must have demonstrated experience of in vivo models of inflammatory disease and a
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flow cytometry and ELISA. You must have demonstrated experience of in vivo models of inflammatory disease and a flexible approach to dealing with research problems as they arise. You must demonstrate
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. At present there is specific interest in advanced 3D perception techniques such as geometric foundation models, implicit neural rendering (NeRF, Gaussian Splatting) as well as semantic mapping. Our research
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operational efficiency. Led by Professor Chris Holmes, the centre will initially focus on the following thematic areas: Decision analysis under model misspecification Uncertainty quantification around LLMs