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SQL databases and file repositories. We are now taking the next strategic step: developing ontologies and a dynamic knowledge graph to semantically link our internal data systems - and connect them
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models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train these methods in a closed-loop
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, optimization, control, game theory, and machine learning. Interdisciplinary by design: Work at the intersection of energy systems and markets, privacy and cybersecurity, forecasting, optimization, control, game
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directions will be pursued to enhance column generation using machine learning. The first line of research focuses on improving scalability by using Graph Neural Networks to identify and eliminate non
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engines. Knowledge of advanced combustion concepts is a merit but not a prerequisite. Practical experience of engine operation in a lab is a merit. Knowledge of control theory is a merit and even better is
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and colleagues working with theory that will complement your work with density functional theory, phase-field simulations, and finite element modelling. Qualified applicants must have: Enthusiasm
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understanding of combustion and engines. Knowledge of advanced combustion concepts is a merit but not a prerequisite. Practical experience of engine operation in a lab is a merit. Knowledge of control theory is a
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cement based on literature, theory, and experiments. Optimization of composition with other waste materials. Thermodynamic modelling and experiments with advanced technologies are used. Development of a