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, UAEs, GANs, diffusion models/flow matching, and Gaussian processes for realistic load, generation and voltage time series. You will embed physical constraints into generation: power-flow consistency
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, UAEs, GANs, diffusion models/flow matching, and Gaussian processes for realistic load, generation and voltage time series. You will embed physical constraints into generation: power-flow consistency
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unable to load from YouTube. Accept cookie and refresh page to watch video, or click here to open video) About the position Are you motivated to take a step towards a doctorate and open up exciting career
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are constructed for a given load (a water level on sea or rivers, or river discharge, with an associated probability of occurrence). Using these probabilities and the consequences, flood risk can be described
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damage and loss of life. These scenarios are constructed for a given load (a water level on sea or rivers, or river discharge, with an associated probability of occurrence). Using these probabilities and
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benchmark chemometric and physics-informed machine learning models to monitor, forecast, and ultimately control critical process parameters, implanting these models in advanced control frameworks to optimize
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. You will find more information about working at NTNU and the application process here. ... (Video unable to load from YouTube. Accept cookie and refresh page to watch video, or click here to open video