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experimental and computational datasets. The overarching objective of this work is to establish predictive, patient-specific models capable of forecasting clinical outcomes in breast reconstruction, thereby
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Location: South Parks Road, Oxford, OX1 3QY Join the Oxford Martin Programme on Forecasting Technological Change at the University of Oxford, led by Dr François Lafond, Prof J. Doyne Farmer, and
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, T., Le Cozannet, G. and Merad, M. (2023) Adapter le système assurantiel Français face à l’évolution des risques climatiques. https://www.ecologie.gouv.fr/sites/default/files/Rapport_final_Mission
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Ellison Institute of Technology, Oxford Limited | Oxford, England | United Kingdom | about 9 hours ago
challenges. Learn more at http://www.eit.org. About Economics at EIT EIT Economics is recruiting full-time Postdoctoral Research Fellows to help build the economic case for transformative solutions to global
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Theory of Large-Scale Structure (EFTofLSS)—to include the effects of massive neutrinos and non-standard dark matter, validate theoretical predictions against simulated datasets, forecast expected
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the world, but extinction because of climate change and human pressures. Our project will use cutting-edge imaging and AI-driven analysis to reconstruct their life histories and forecast future reef
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phenomenon for decision-making or to obtain tighter constraints on a model (e.g., in weather forecasting). In this context, jointly considering data acquired by different instruments on the same objects has
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widely in e.g. safety-critical (embedded) systems, data analysis, weather forecasting, physics, and engineering. Verifying that such programs are correct is challenging because of rounding errors due
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Forecasts (ECMWF) in delivering high-quality climate data and services. The service contract builds on previous C3S activities, ensuring continuity and reliability during a period of global uncertainty
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). This position focuses on the machine learning methodology of the project, aiming to: Develop probabilistic spatio-temporal models that integrate uncertainty from climate projections into land-use forecasts