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to the 4th February 2026. You will be investigating the safety and security implications of large language model (LLM) agents, particularly those capable of interacting with operating systems and external APIs
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and optimising assays aimed at target validation; principally through immunogenicity assays in animal models. You will also conduct experiments aimed at understanding the tumour-immune microenvironment
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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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form, how it is generated and how it evolves. In particular we focus on the evolution and evolvability of vertebral counts, and we use various species of Lake Malawi cichlids as our model organism
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on evaluating the abilities of large language models (LLMs) of replicating results from the arXiv.org repository across computational sciences and engineering. You should have a PhD/DPhil (or be near completion
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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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cancer progression, immune evasion, and therapeutic resistance. We place a strong emphasis on the use of spatial biological approaches applied to human tumour models including organ/tumour perfusion, slice
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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 discispline. You
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should have a PhD (or close to completion) in Physics, Planetary Sciences or Earth Sciences. It will be an advantage to have experience in remote sensing, analysis of thermal data, thermal modelling
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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