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Field
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The successful candidate will join the Climate Physics team at ENS de Lyon (https://climatephysics-ensl.fr/ ), which currently consists of 5 permanent researchers, 7 PhD students and 2 postdoctoral researchers. We
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deployed. The research will therefore combine qualitative fieldwork with computational modelling to develop approaches that operate under uncertainty, adapt as the landscape changes, and generalise beyond
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submit their application incorrectly or who do not prove the requirements demanded in this competition shall be excluded from admission. The jury is entitled to require any candidate, in case of doubt
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from aquatic sediments represent a major uncertainty in the global carbon cycle. This project seeks to advance our mechanistic understanding of methane gas dynamics across multiple spatial scales by
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discovery and inference, with an emphasis on robustness, scalability, uncertainty quantification, expert knowledge integration, and multi-scale causal abstraction and representation learning. Your Job How are
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 1 month ago
, how it can be extracted, and how confidently conclusions can be drawn. Develop and apply approaches for uncertainty quantification, robust inference, and validation of biomedical imaging results. Work
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increasingly complex, data-rich, and dynamic. Public transport operators, freight carriers, and logistics providers must make decisions under uncertainty while responding to disruptions, fluctuating demand, and
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on: https://www.dges.gov.pt/pt/pagina/reconhecimento Workplan and the objectives to achieve: The work to be carried out consists of the development and experimental validation of control and estimation
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received funding from the European Innovation Council and the European Union’s Horizon Europe research and innovation programme under Grant Agreement No.101306664. Where to apply Website https
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design spaces, long-duration simulations, and real-time applications. This PhD project will develop physics-informed deep learning and surrogate modelling approaches to accelerate simulation, uncertainty