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experiments, mapping charge and energy transfer in molecular (and mesoscopic) systems, behavior of molecules in strong fields, dynamics of open quantum systems, and more. This project offers an excellent
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learning potentials (MLPs). You will develop geometric graph manifold learning methods and utilize those to learn from heterogeneous quantum mechanical datasets. You will implement and benchmark state
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, targeting applications in the fields of communication, sensing, geolocalization, space and biomedical. This Ph.D. project will take place at DTU Electro. Apart from the time at DTU there will be secondments
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of neuromorphic computing and analog signal processing, targeting applications in the fields of communication, sensing, geolocalization, space and biomedical. This Ph.D. project will take place at DTU Electro
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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