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architecture (i.e. hydrogeological schematizations) that encompass the 3D variability in geohydrological properties. These models are to be validated using real-world monitoring data (e.g. on sand boils), and
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semantic change and synonymy evolution across time. developing hierarchical clustering architectures to identify and track polysemous senses in historical corpora. applying the framework to English (COCA
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process knowledge into modern AI tools. The research offers the opportunity to explore neural network architectures, tabular transformers or Bayes methods to include process-information into machine
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of the subsurface architecture (i.e. hydrogeological schematizations) that encompass the 3D variability in geohydrological properties. These models are to be validated using real-world monitoring data (e.g. on sand