13 distributed-algorithm Postdoctoral positions at University of Oxford in United Kingdom
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navigation algorithms and machine learning models on physical robot platforms. We are particularly interested in candidates with expertise in generative AI and curriculum learning applied to robotics, as
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movement; (iii) generate benefits for both society and the environment by guiding possible mitigation strategies; and (iv) drive technological progress through the development of novel algorithms
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**AAV vector; • Transfection of endothelial cell-derived extracellular vesicles with **GENE**-mRNA; • In vivo delivery of first vector and evaluation of expression/distribution
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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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to reconstruct the tree-of-life on Earth, it allows us to reveal how biological function has evolved and is distributed on this tree, and it is the foundation that enables us to use model organisms
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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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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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. Armed with this information, the post holder will use cutting-edge paleoclimatic modelling that incorporates nutrient cycling and carbon chemistry (HadOCC) to infer the distribution of potential feeding