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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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the network, as well as with academic and non-academic partners. General information about the CLIMES project is available at: [https://www.climes.se/climesdn/ ](https://www.climes.se/climesdn/ ) All working
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predictions against simulated datasets, forecast expected constraints from Euclid and SKAO, and ultimately analyze the Euclid spectroscopic galaxy sample in its second data release. Collaboration Framework and
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numerical forecasts, and strengthening science-society interfaces by co-developing climate service demonstrators and sharing knowledge with stakeholders. Given the dramatic increase in dengue incidence over
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bifurcation has already been observed. However, a precise understanding of the origin of those bifurcations is still lacking, preventing the forecast of such events. One possibility is that the triggering