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related field together with strong programming skills in Python, R, or similar languages, and proficiency in high-performance computing. You will have experience in large-scale genomic data analysis. You
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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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to analyse datasets Experience in statistical or scientific programming (ideally R and/or Python) Experience in analyzing large and/or complex datasets Interest in quantifying uncertainties for computer models
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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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equivalent up to and including grade 7. Visit the Centre for Research Staff Development for more information. About You To be successful in this role, we are looking for candidates to have the following skills
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and archival data, as well as working towards future observing proposals and strategies. Within this framework, the successful candidate will have flexibility and freedom to tailor the project
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About the Role You will develop and apply novel computational methods to quantify the societal impact of fundamental science discoveries. Candidates close to completion of their PhD will initially
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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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supervise junior researchers. Uphold best practice in ethics, data management, and research governance. About You To demonstrate at least one of the following : A PhD (or near completion) in computer science
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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