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Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The
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AI researchers from ANITI, IMT and CERFACS, as well as with researchers/engineers in weather forecastings from the CNRM (Météo-France). Hybridization methods between neural networks and physical models
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of agricultural innovation, exploring how advanced weather forecasting, crop modelling, and digital decision tools can be harnessed to support smarter, more agile on-farm decisions. It offers a unique opportunity
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study on the Ouseburn in Newcastle upon Tyne to demonstrate its capability for real time water quality forecasting and its ability to support decision-making aimed at protecting river users from health
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PhD Studentship: Real-Time River Water Quality Forecasting through Integrated Hydrodynamic and Surrogate Modelling Award Summary This studentship provides a tax-free annual living allowance
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have since helped halve global mortality, but this progress is threatened by rising insecticide resistance. We build quantitative, data-driven models to forecast the spread and impact of resistance
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of hazard assessment. North- Atlantic cyclones can cause severe damage to the neighbouring land of North America and Europe. Increasing the lead time of seasonal forecasts will allow for governments
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challenges in the area of hazard assessment and impact forecasting. The aim of the project is to develop methodologies for forecasting future energy use for various assets and weather scenarios from short term
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experience in the processing and integration of massive datasets (‘big data’), they will contribute to ecological modelling, environmental impact forecasting, and soil biodiversity analysis in the central
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systems such as flashback which can occur with hydrogen or blow-off with ammonia. Currently, we cannot accurately forecast such extreme events due to the chaotic nature of the underlying turbulent flows and