350 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "The Institute for Data" positions at University of Oxford
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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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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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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
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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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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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students in the group. Candidates should have strong training in cross-disciplinary applied mathematics, with a demonstrated interest in biology, and experience in machine learning approaches is a plus. We
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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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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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turbines represented using an actuator-line approach, assess the applicability and limitations of reduced-order models in predicting turbine performance, and develop machine-learning surrogate models capable