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/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines for plausible narratives of regional climate change, novel algorithms for rare
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for plausible narratives of regional climate change, novel algorithms for rare event sampling or ensemble boosting, and the development and use of hybrid climate models combining physics-based and ML components
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algorithms into an existing framework, with a focus on efficiency, as well as creation and execution of relevant simulation pipelines: from real data to mathematical and clinically actionable results
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in their computation. We want to understand the fundamental principles that permit us to build privacy-aware AI systems, and develop algorithms for this purpose. The group collaborates with several
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reconstruction algorithms that incorporate multiply-beam coherent scattering imaging in a grazing incidence geometry to improve the spatial resolution to ultimately demonstrate the utility of the novel coherent
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. The project will involve development of novel parallel algorithms to facilitate in-situ analyses at-scale for multi-million and multi-billion atom simulations. In this role, you can expect to work on enhancing
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-ordinate multiple aspects of work to meet deadlines. The post will be based in the Department of Chemistry, Chemistry Research Laboratory, 12 Mansfield Road, OX1 3TA and is available from 1 October 2025
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computational workflows on a high-performance cluster. You will test hypotheses using data from multiple sources, refining your approach as needed. The role also involves close collaboration with colleagues
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-Gurevich Probability, random walks, random graphs, probabalistic algorithms Genadi Levin Complex and one-dimensional dynamics, non-linear phenomena Noam Lifshitz Extremal combinatorics, Additive
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and sets up experiments in hybrid research environment. 2. Researches artificial intelligence/machine learning algorithms, database design, deep learning, big data, and cloud computing. 3. Publishes