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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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-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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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
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Chris Holmes, the centre will initially focus on the following thematic areas: • Decision analysis under model misspecification • Uncertainty quantification around LLMs • Constrained
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performance in heavy industry. You’ll develop and apply state-of-the-art modelling, characterisation, and machine learning techniques to understand how batteries behave and age. Collaborating with project