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settings. We are seeking a highly motivated postdoc to conduct research into this fast-moving area. Directions may include investigating quality evaluation methods for multi-agent systems, attack surfaces
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the tree of life. The main responsibilities will be to identify ancient gene families that encode membrane proteins and then use a range of phylogenomic methods to understand their ancestry. These analyses
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learning approaches. You will develop novel, reproducible methods for analysing both structured and unstructured clinical data, generating insights into disease trajectories, predicting clinical outcomes
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on qualifications and relevant skills acquired and will also be determined by the funding available. About you Applicants will hold a PhD/DPhil or be near completion of a PhD/DPhil in a subject relative to Structural
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, available for up to 30 months, tenable immediately, to conclude before May 31st 2028. Intelligent agents have a venerable history in AI: since the 1980s the problem of how to build hardware and/or software
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explores novel aggregation methods at the intersection of AI safety, computational social choice, and judgment aggregation, aiming to formally integrate multi-stakeholder preferences into AI system design
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, delivering tested methods, and creating algorithms to expand MMFM capabilities across domains like cardiology, geo-intelligence, and language communication. The postholder will help lead a project work package
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will also contribute to or write research articles at an international level for peer-reviewed journals. You will be responsible for formally presenting your research and represent the research group
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in Mass Spectrometry and Structural Glycobiology to work under the supervision of Prof. Weston Struwe for a period of 24 months. The project, funded by the UKRI, centres on developing advanced methods
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), to develop systems that improve the efficacy of machine learning-based technologies for healthcare applications. You must hold a PhD (or be near completion) in a field such as AI, computer science, signal