20 mathematics-graph-theory Postdoctoral positions at Technical University of Denmark in Denmark
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create multi-fidelity predictive models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train
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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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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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candidate to help develop these new manners by which to promote bonds as kernels in the interpretation of chemical simulations. For this purpose, novel theory and simulation software will need to be developed
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mathematical ability Experience with symmetry analysis, group theory, or topological band theory Demonstrated experience in programming and contributions to open-source software Excellent communication skills
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for security and export control, open-source background checks may be conducted on qualified candidates for the position. DTU Compute DTU Compute – Department of Mathematics and Computer Science – is an
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, which includes mathematics, computer science, physics, chemistry and biology, provides the foundation for new and innovative technology for the future. Technology for people DTU develops technology for
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equivalent). Other qualifications and competences include: Experience with bioinformatics Experience with mathematical modelling and programming, including source attribution modelling of foodborne pathogens
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, you will contribute to research-based teaching and the supervision of student projects. Skills in mathematical modelling and machine learning of relevant physical glacier processes (ice sheet and