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for the study of the history of the world. We are an intellectual home for scholars of every region of the world, who use approaches which range from local micro-histories to large-scale quantitative
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challenges of our time, with rising ideological divides and fragmented information ecosystems coinciding with increasing stress, anxiety, and declining well-being. Polarizing online content not only fuels
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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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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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targets. You will take a lead on development of computational approaches to integrate multi-omics data from patient samples, including DNA methylation, histone modifications, single-cell transcriptomics and
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networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity rules which enable effective learning in large and deep networks and is consistent with
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, spanning over 400 boxes, 100 volumes, and a large historical book collection, holds critical documents related to its colonial and financial history. A significant portion will return from the University
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, Research and Teaching Technicians, Teaching Fellows and AEP equivalent up to and including grade 7. Visit the Centre for Research Staff Development for more information. About You To be successful in
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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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. They will also help coordinate and implement future field campaigns to fill key field data gaps. Additionally, the successful candidate will be responsible for advancing cutting-edge methods for quantifying