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Field
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UKESM1 or similar models, advanced data analysis and machine learning, would be advantageous. Grade E: You will be near completion of a relevant PhD or have equivalent research experience, and be able
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relevant data science and machine learning tools. Able to work independently and comfortably with a team and external/international collaborators. Able to handle multiple tasks relevant to both project and
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machine learning Personal and relational qualities will be emphasized. Motivation, ambitions and potential will also count when evaluating the candidates. Special requirements for the position
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written and oral English. Experience from one or several of the following areas is an advantage: Programming, image processing and machine learning. Magnetic Resonance Imaging. Laboratory experience from
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properties of the Higgs boson. The group focuses on final states containing several tau-leptons. The analysis activity is now extended to include generic anomaly searches using Machine Learning. Furthermore
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qualification/experience in a related field of study. The successful applicant will have expertise in statistical modelling, epidemiology or machine learning and possess sufficient specialist knowledge in
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machine learning. Magnetic Resonance Imaging. Laboratory experience from porous media research related to physics and/or chemistry. Personal and relational qualities will be emphasized. Motivation
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Applicants are invited for the posts of Research Associate or Research Fellow in Machine Learning to work with AI Researchers in the Centre for AI Fundamentals at the University of Manchester. You
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anomaly searches using Machine Learning. Furthermore, the group takes part in ATLAS upgrade, with participation in the ITk-Pixels project, with responsibilities concerning testing and delivery of pixel
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tools such as R, Python, or MATLAB as well as relevant machine learning frameworks Experience in statistical data analysis, and expertise in areas such as experimental design, linear/nonlinear models