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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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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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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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-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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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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theory, symmetry analysis, and group theory. You will work on developing and applying these ideas to discover new photonic phenomena, implement associated computational tooling, and to find opportunities
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. During the project, you will work closely together with colleagues in theory who propose optimized designs for your devices, and both receive and provide help to fellow colleagues performing
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partners and optimize them for nonlinear quantum processes Collaborate with theory colleagues to refine fiber designs based on experimental feedback Disseminate results at international conferences and in