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of Oxford. The post is funded by the National Institute for Health and Care Research (NIHR) and is fixed term for 24 months. The researcher will develop multi-sensor 3D reconstruction algorithms to fuse
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We invite applications for a full-time Postdoctoral Research Associate to join the new Data-Driven Algorithms for Data Acquisition (DataAcq) project. This is a timely project developing new
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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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, delivering tested methods, and creating algorithms to expand MMFM capabilities across domains like cardiology, geo-intelligence, and language communication. The postholder will help lead a project work package
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aims to develop formal frameworks and algorithms for eliciting, aggregating, and analysing stakeholder preferences over risk and safety in AI systems. The Research Assistant will support the development
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and evaluation. The post holder will take a leading role in advancing theoretical and algorithmic research in the domain of probabilistic preference aggregation, contribute to the design and analysis
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Engineering, Mathematics, Statistics, Computer Science or conjugate subject; strong record of publication in the relevant literature; good knowledge of machine learning algorithms and/or statistical methods
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distributed quantum computation. In this project, we plan to push all of these areas further, with experiments in blind quantum computing, quantum repeaters, and enhanced metrological quantum advantage. We seek
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atmospheric plasmas are inaccurate and do not produce the correct compositions and energy distributions of expanding flows. The proposed research aims to make a step change in domestic and international
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of original machine-learning based algorithms and models for multi-modal ultrasound guidance that are intuitive for a non-specialist to use while scanning and trustworthy. You will work with clinical domain