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explored using scientific machine leaning. Machine learning, programming experience and a curious mind-set You are fascinated by how computers can learn from data and you have a strong interest in the
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. The position is an Alliance PhD partially funded by DTU and partially funded by Villum Fonden under the Villum Young Investigator program. The PhD project will investigate the molecular basis of enzyme-substrate
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competences within computational modelling, optimization and integration of thermal energy storage technologies – such as large water pits and phase change material storage. You will work with colleagues, and
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terms of purity, efficiency and photon indistinguishability (PhD2). Supervise MSc/BSc/Fagprojekt project students. Contribute to the development of new SPS designs. Document the research with scientific
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; characterization of supramolecular polymers, their assembly, triggered disassembly, structure and properties. Active dissemination of the results obtained, in scientific articles and through participation in
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Materials Science and engineering or chemistry or related subjects such as corrosion science and engineering. Masters in electronics engineering with prior experience on humidity/corrosion reliability