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Computer Science, Southampton. The project is researching, developing and evaluating decentralised algorithms, meta-information data structures and indexing techniques to enable large-scale data search
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of data on invasions, their impacts and their management. The scientist will also analyse data to quantify the costs and benefits of the use of nonnative trees in forestry. They will use these analyses
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opportunities, we anticipate a large number of applications at CSER; due to the limited amount of workspace available, we can only forward a small selection of these to the British Academy. Applications should be
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the disease, using novel methods for analysing complex high-dimensional genetic, transcriptomic and proteomic data linked to health outcomes from large biobanks and smaller clinical cohorts. The Opportunity: We
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From a Bletchley Park code-breaker to Nobel Prize-winners and the inventor of the phrase ‘Big Bang’, Research Fellows of St John’s have made remarkable contributions to the world. Now applications
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WMG collaboration with a large vehicle manufacturer. About You You will need a good honours degree and a PhD (or be close to submission thereof) in a relevant subject, and experience of the theory and
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CRISPR and other molecular genetic technologies to study infection dynamics. Analyse Complex Data: Apply computational and biostatistical methods to analyse large data. Collaborate Across Disciplines: Work
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the Medical Research Council. The Research Fellow will be using Natural Language Processing (NLP) methods, with a special focus on generative Large Language Models (LLMs), to interrogate a very large sample of
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large amounts of complex data. They should provide evidence of potential for research and publication at the highest level, as well as a strong interest in engagement in empirically-grounded, quantitative
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researcher with a PhD in Soil Science or Agricultural Science, possessing strong field research skills, data analysis expertise, and the ability to work collaboratively in an international, multidisciplinary