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Field
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quantum chemistry (QM), machine learning (ML), and high-throughput experimentation (HTE). The objective is to develop a data-driven framework that enhances the efficiency and effectiveness of catalyst
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oxides, hydroxides and hydrides using a combination of solid-state density-functional theory (DFT) and machine-learning force fields (MLFFs). DFT methods will be used to study materials of interest
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up exciting career opportunities? Are you interested in cable technology and condition monitoring, and do you have strong competence in signal processing and machine learning? As a PhD candidate with us, you