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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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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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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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Professor Chris Russell. This is an exciting opportunity for you to work at the cutting edge of AI, contributing to a major shift in how we understand and apply foundation models. The position is full-time
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structural), ECG, and genetics, to model disease trajectories and improve risk prediction in cardiomyopathies. The successful applicant will work closely with the PI to deliver research projects, supervise
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mechanisms for grid-edge device federations participating in local and national flexibility markets. This will bring together research on power systems modelling, multi-agent control, machine learning and
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Haematology Unit. You will use state-of-the-art genetic tools and functional genomics to generate and characterize models of CH and ageing, including the role of the bone marrow microenvironment in
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base, the partnership will bring together the University of Oxford’s expertise in statistics, mathematics, engineering and AI with industry scientists. Within the partnership, small research teams will
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in preventing immune-mediated pathology in autoimmunity remains poorly understood. Using genetic and antibody-based targeting, we aim to dissect how these pathways modulate T-cell signalling