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supervisors and industry partners, you will bridge theory and practice to create research with real-world impact. Your primary tasks will be to: Conduct research on data-driven and modular approaches
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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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learning architectures including generative models, particularly for sequence or structural data (e.g. transformers, graph neural networks) Proved experience in working independently and as part of a
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Familiarity with asynchronous workflows and task queues (e.g., Celery) Knowledge of database design, preferably with relational or graph-relational models (PostgreSQL, EdgeDB/Gel, etc.) Experience working with
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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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development, integrating quantum communication into classical communication courses, emphasizing both theory and experimental skills, and bridging the knowledge gap between the two fields. Public outreach and
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practice as well as in theory. Your primary responsibilities will include: Investigate, develop and validate new models to simulate wind turbine response in HAWC2 at different fidelity levels. Validation
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-assisted control of large wind turbines. You will collaborate closely with both academic and industrial partners in Denmark and abroad, ensuring that your research has impact in practice as well as in theory
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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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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