246 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"IFM" positions at University of Oxford
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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
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thrusts within the lab’s multi-agent security programme. You should possess a completed PhD/DPhil (or thesis submitted by the start date) in Computer Science, Machine Learning, AI, Security, Robotics
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sciences, AI, machine learning or related fields. Strong background and track record in the development of geospatial foundation models from multi-modal Earth Observations is essential as well as strong
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dealing with customers, both face-to-face and by telephone and e-mail as appropriate. Ability to use standard computer programs (Outlook, Word, Excel) To demonstrate good communication skills (both oral and
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for the project’s goal and a willingness to learn and develop new skills as the work evolves. Given the highly interdisciplinary nature of the role, you will be expected to collaborate closely with researchers from
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An exciting opportunity has arisen for a Postdoctoral Research Assistant in the Department of Physics. Machine learning has made enormous progress during recent years, entering almost all spheres
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responsible for supporting the delivery of various foresight research projects the Centre will be undertaking. This is an excellent opportunity to gain academic research experience and to learn from leading
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5 years. The appointments will be in the area of statistical quantitative finance/financial econometrics, in particular data science and machine learning applied to quantitative finance