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sensing, signalling and memory, critically influences the disease onset and progression1. The Iskratsch Group , at the School of Engineering and Materials Science, Queen Mary University of London is
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-edge machine learning techniques will be used, including Large Language Models (LLMs). About Queen Mary At Queen Mary University of London, we believe that a diversity of ideas helps us achieve the
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. The project looks at the political representation of men, to see how men are represented and by whom. About Queen Mary At Queen Mary University of London, we believe that a diversity of ideas helps us achieve
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students. It is one of five schools in the Faculty of Science and Engineering at Queen Mary University of London. These are supported by an administrative and technical staff team of 45. The staff and
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conducted archival research with 16th and 17th-century collections is essential. This experience need not have been gained in a university role. Comparable experience gained in heritage, secondary education
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of having conducted archival research with 13th and 14th century collections is essential. This experience need not have been gained in a university role. Comparable experience gained in the heritage
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2025. We seek to recruit a Research Associate specialising in statistical modelling and machine learning to join our multi-university multi-disciplinary team developing a groundbreaking technique based
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group including research assistants, PhD and MSc students, project volunteers and other members of the team. About Queen Mary At Queen Mary University of London, we believe that a diversity of ideas helps
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Systems, and the Centre for Mathematical Education. About Queen Mary At Queen Mary University of London, we believe that a diversity of ideas helps us achieve the previously unthinkable. Throughout our
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. BCI is committed in supporting and developing future cancer researchers through its extensive postgraduate training. About Queen Mary At Queen Mary University of London, we believe that a diversity of