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on the intersection between diffeomorphic models of shapes, related geometric theory, and statistics and machine learning, e.g. generative models. Examples of specific topics in this span include Bayesian models
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functional theory and ab-initio molecular dynamics simulations) with artificial intelligence techniques to parameterize machine learning force fields and kinetic Monte Carlo methods to model the molten salt
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-funded competence centre for Artificial Intelligence and Machine Learning. It now consists of more than 40 excellent research groups, both in the field of application-oriented Machine Learning and in
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(density functional theory and ab-initio molecular dynamics simulations) with artificial intelligence techniques to parameterize machine learning force fields and kinetic Monte Carlo methods to model
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), consists of two main parts. First, the candidate will develop machine learning models aimed at improving the follow-up of neurocognitive function in critically ill children after discharge from the intensive
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The molecular biosciences are undergoing a major paradigm shift – away from analysing individual genes and proteins to studying large molecular machines and cellular pathways, with the ultimate goal
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Detection and Machine Learning (IEL) PhD in Power Grid Modelling for Net-Zero Energy Systems (IEL) PhD in Incorporating Distribution Grids in Multiscale Stochastic Energy System Models (IØT) PhD in Aspects