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plasticity platform. Different machine learning strategies will be explored to capture the complex relationships between microstructural features and mechanical responses. In particular, the project will
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induced seismicity. Current models remain limited by the scarcity, heterogeneity, and noise of available data, as well as by incomplete knowledge of the subsurface. Physics-Informed Neural Networks (PINNs
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chronic inflammatory disease. These diseases affect 33%, 60%, 10% and 33% of Europeans, and are responsible for >60% of global deaths. The European Doctoral Network LipidBRIGHT combines scientists
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or other large-scale biological data), using statistical methods, pathway/network analysis or machine learning. The candidate will conduct integrative analyses of biomedical datasets, focusing on single-cell
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the German tradition (e.g., Kant, Humboldt, Goethe) 3. Its complex reception in Germany and beyond 4. Its contemporary relevance for environmental studies and scientific discourse Project activities include
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of ecological thought in 19th-century European evolutionism 2. Its philosophical sources in the German tradition (e.g., Kant, Humboldt, Goethe) 3. Its complex reception in Germany and beyond 4. Its contemporary
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, cultural and professional institution. Member of the Ecoles Centrales group and the Écoles Nationales d’Ingénieurs network,ECL trains high-level generalist engineers, specialized engineers, masters students
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Institut National des Sciences Appliquées de Lyon | Villeurbanne, Rhone Alpes | France | 14 days ago
ABHSSYS (Acoustic Black Holes for Silent SYStems, Grant agreement ID: 101227712) is a European Doctoral Network funded by the Marie Skłodowska-Curie Actions (MSCA), dedicated to advancing innovative
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embedded in the Doctoral Programme in Complex Systems Science at the University of Luxembourg. The modelling approaches developed in this project share conceptual similarities with adaptive network and
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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