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-holomorphic Hilbert Modular Forms”. The central aim of the project is to develop explicit algorithms for computing with non-holomorphic Hilbert Modular Forms and using these algorithms together with theoretical
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algorithms, develop detailed data analysis plans and contribute to writing technical documentation and scientific reports. The Research Fellow - Health Data Scientist will help plan and participate in regular
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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences
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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences
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highly motivated researcher to develop artificial intelligence based novel algorithms and computational workflows to predict the impact of mutations on genes in the avian flu virus and the viral host which
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binding pockets. About the role We are seeking a highly motivated researcher to develop artificial intelligence based novel algorithms and computational workflows to identify domain functional families
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-fostering prototypes through co-design with children, creating evaluation frameworks to assess children's algorithmic agency in educational contexts, and establishing design principles that prioritize
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to) fundamental research in machine learning or statistics, algorithm design, the application of AI methods in science, healthcare, social sciences, or business. You should have a PhD or equivalent level of
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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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the prevalence and risk of modern slavery. There will be a focus on Bayesian nonparametric methods and practical development of MCMC algorithms that can be applied to data. Translating the project findings