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months. The project involves the scale-up of new hierarchical metal oxides and hydroxides and is funded by the UCSF (Oxford University Challenge Seed Fund). Find out more about the O'Hare research and
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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 project will focus on developing a thermal water splitting process based on complex transition metal oxides, and then studying the kinetics of the process to facilitate the design of a reactor to integrate
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focus on ambitious, ‘blue sky’ research for novel methods development relevant for drug discovery analysis pipelines, trial design and operational efficiency. Led by Professor Chris Holmes, and with
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settings. We are seeking a highly motivated postdoc to conduct research into this fast-moving area. Directions may include investigating quality evaluation methods for multi-agent systems, attack surfaces
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nanotubes, enables the exploration of thermodynamic processes at the nanoscale. Carbon nanotubes serve as exceptional nanomechanical resonators due to their low mass, high stiffness, and quality factor
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learning approaches. You will develop novel, reproducible methods for analysing both structured and unstructured clinical data, generating insights into disease trajectories, predicting clinical outcomes
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discoveries on the electrosolvation force. The project will use a range of optical methods to examine the interactions in colloidal and molecular systems and relate the experimental findings to theories
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of advanced X-ray methods to explore chemical, crystallographic and morphological changes that drive battery performance loss. You should possess a doctorate in a relevant engineering or physical science
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Engineering, Mathematics, Statistics, Computer Science or conjugate subject; strong record of publication in the relevant literature; good knowledge of machine learning algorithms and/or statistical methods