371 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" positions at University of Oxford in United Kingdom
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We are looking for a Postdoctoral Research Associate reporting to the Principal Investigator Prof Yee-Whye Teh, they will be a member of the Oxford Computational Statistics and Machine Learning
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service, has an appetite to learn, is keen to understand and support a wide range of technology and has outstanding written and verbal communication skills? We’re looking for an enthusiastic, customer
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modelling, and machine learning approaches to analyse large-scale datasets, including bulk and single-cell sequencing, gene expression arrays, proteomics, and metabolomics. Working closely with senior
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computational statistics, machine learning, theoretical statistics, and probability as well as applied statistics fields, including statistical and population genetics, bioinformatics, econometrics, statistical
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wide range of libraries/frameworks and cloud infrastructure tools. Familiarity with Machine Learning, Neural Network and AI driven image analysis methods Experience with data management for large data
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the Photonic Quantum Information group (PQI) led by Professor Ian Walmsley in the area of quantum machine learning. You will engage in advanced study and academic research, working to develop and carry out
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publishing work as lead author. Experience with machine learning methods for modelling human learning, such as knowledge tracing and/or experience with conducting research that involves prompting or fine
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productivity and output • Learn and be fully independent on the existing laboratory workflows, such as transcriptomics analytical pipelines running on Python such as Topometry • Manage own academic
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willingness to learn new skills and undertake further training and development aligned to the role, which may include working with laboratory animals Experience in working in a scientific laboratory, including
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) in Physics or a related field. Previous experience in cosmological simulations, analysis of cosmic microwave background and/or large-scale structure datasets, machine learning methods applied