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Field
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-resource settings. This project aims to achieve several objectives, including the development of a new AI-algorithm and a paired dataset for comparing how different imaging techniques influence
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formulation, which displays striking similarities to that used by the Computational Fluid Dynamics (CFD) community, has inspired the investigators to adopt conventional CFD algorithms in the novel context
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quantitative analysis skills and experience developing algorithms and/or conducting statistical analyses with biological datasets. Background and work knowledge in statistics, algorithms, optimization of novel
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. By integrating cutting-edge multi-agent systems, federated learning, and game theory, this project will develop sophisticated decentralised algorithms that enable autonomous vehicles (AVs
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apps and software algorithms would be desirable, as would an understanding of major depression and its pharmacological treatments. Diversity Committed to equality and valuing diversity Our active
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. The task of the theory group led by Prof Kyriienko at the University of Sheffield within the consortium is to lead nationally the development of quantum machine learning (QML) algorithms. The research will
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scientific publications, patents, and seeing collaborators translate our work into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and
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into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and applications (e.g. natural language processing, multivariate time-series data
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mix of algorithm engineering and formal methods, alongside more traditional software engineering activities. This project involves developing software which is both mathematically rigorous, and
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or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental