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mountain catchments. The goal is to understand long-term changes in runoff regimes and flood hazards by combining climate-driven (glacier-)hydrological modelling with reconstructions of past flood events
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will contribute to developing knowledge-driven decision-making models. The postdoctoral research fellow will actively participate during the supervision process of PhD-students as main or co-supervisor
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techniques such as signal processing and dynamic systems modeling, and will contribute to developing knowledge-driven decision-making models. The postdoctoral research fellow will actively participate during
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invites applicants for four PhD Fellowships in subsurface characterization within geosciences, reservoir engineering, molecular modelling, and machine learning at the Faculty of Science and Technology
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models, aiming to reduce CO₂ emissions and improve resource efficiency through enhanced data-driven lifecycle management. A DPP can be viewed as a structured, machine-readable knowledge artifact
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networks in order to enhance fog net technology. The planned work is experimental and will be conducted in our lab facilities, also incorporating theoretical models of complex flow. Fieldwork is planned in
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“Embedded Sufficient Statistics” . This position is placed at Integreat - Norwegian Centre for Knowledge-driven Machine Learning is a Centre of Excellence, funded by the Research Council of Norway
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placed at Integreat - Norwegian Centre for Knowledge-driven Machine Learning is a Centre of Excellence, funded by the Research Council of Norway. Researchers at Integreat develop theories, methods, models
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