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development to work under the supervision of Dr Alistair Farley, Scientific Lead for Chemistry, with a dotted line to Professor Timothy Walsh. The position is based at the Ineos Oxford Institute, at the Life
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evaluations, attacks on and defensive mechanisms for safe multi-agent systems, powered by LLM and VLM models. Candidates should possess a PhD (or be near completion) in Machine Learning or a highly related
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) information-theoretic active learning, and c) capturing uncertainty in deep learning models (including large language models). The successful postholder will hold or be close to the completion of a PhD/DPhil in
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, or computational modelling. This post is based at the Department of Computer Science and on-site working is required. Remote and part-time working options must be agreed with Professor Nobuko Yoshida. What We Offer
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quality refereed journals and write reports for submission to research sponsors. You must hold a PhD (or be near completion) in a biomedical field of laboratory-based research (ideally immunology and/or
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fundamental algorithms for producing policies for rich goal structures in MDPs (e.g. risk, temporal logic, or probabilistic objectives), and modelling robot decision problems using MDPs (e.g. human-robot
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choice theory, or computational modelling. This post is based at the Department of Computer Science and on-site working is required. Remote and part-time working is possible in agreement with Professor
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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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interpretation of atmospheric circulation in high-resolution reanalysis data, idealised model simulations and a state-of-the-art weather forecasting system. The post-holder will have the opportunity to teach
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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