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are looking for a highly motivated and skilled PhD researcher to work on graph-based machine learning surrogates of wind energy systems. Our goal is to accelerate flexible fatigue load estimation
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multimodal machine learning. Admission requirements The general admission requirements for doctoral studies are a second- cycle level degree, or completed course requirements of at least 240 ECTS credits
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involves the use of quantum chemistry, machine learning, and genetic algorithms to search for new homogeneous chemical catalysts. Who are we looking for? We are looking for candidates within the field
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multidisciplinary research in energy markets, optimization, game theory, and machine learning. Our team of 13 members (link ), from 10 different nationalities, values diversity and includes experts from a range of
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networks and machine learning strategies for the analysis of scattering data. Large amount of scattering data obtained in our group requires development of the advanced analysis techniques. In this project
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. - Neural networks and machine learning strategies for the analysis of scattering data. Large amount of scattering data obtained in our group requires development of the advanced analysis techniques. In
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experience with scientific computing, data analysis, machine learning and/or AI You have an interest in environmental sustainability and pharmaceutical production Considered a plus: You have experience with
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calculations of well-characterized 2D materials, simulations of electron microscopy images, and machine learning methods to reconstruct the 3D atomic positions of materials from a 2D microscopy image. The
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. Alternative approaches are graph-based molecule reaction space sampling and generative machine learning as they provide a path to new synthetic data that can form the basis for a large-scale database of
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band score of at least 6.5, internet. TOEFL test (TOEFL-iBT) showing a score of at least 90, or a Cambridge CAE-C (CPE). For additional information, please contact Prof. Dr. Erik Koffijberg