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Learning Research (JMLR), Transactions in Machine Learning Research (TMLR), and IEEE Big Data. Methodological areas of particular interest to our laboratory include (1) probabilistic modeling
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(e.g. a transferable ML for flexibility forecasting) with a focus on designing a robust and scalable ML tool that can be deployed across our various pilot sites in the EU and the UK. The Research Fellow
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(AFESP) - a £30million 15-year investment by the University of Reading, in partnership with the European Centre for Medium-Range Forecasts (ECMWF), the (UK) Met Office and the National Centre for
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£30million 15-year investment by the University of Reading, in partnership with the European Centre for Medium-Range Forecasts (ECMWF), the (UK) Met Office and the National Centre for Atmospheric Science. The
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in the following areas: Deep Learning, Scientific Machine Learning, Stochastjc Gradiant Descent Method, and Numerical PDE’s - Advised by Dr. Yanzhao Cao Probabilistic Graph Theory (Network Traversal
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hybrid manner. We are also open to discussing flexible working arrangements. Would you like to help improve how we forecast volcanic eruptions? Do you have experience in understanding deformation of active
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to be undertaken in a hybrid manner. We are also open to discussing flexible working arrangements. Would you like to help improve how we forecast volcanic eruptions? Do you have experience in
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NorSink will lead to a step change in the emerging science of carbon budgets and forecasting, and provide decision makers with the knowledge to design effective climate policies as Norwegian and European
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(UK). The successful appointee will be centrally involved in the delivery of the ARIA-funded Forecasting Tipping Points programme , and will work collaboratively with Professor Doug Benn (University
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management and oversight of data repository activities; the planning and reporting of analyses and progress reports; preparation of budget reports and budget forecasting; and supervision and training