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combining state-of-the-art machine learning with physicochemical knowledge and molecular modeling. Representative publications from our group include: https://doi.org/10.1038/s41467-025-63947-5 https
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combining state-of-the-art machine learning with physicochemical knowledge and molecular modeling. Representative publications from our group include: https://doi.org/10.1038/s41467-025-63947-5 https
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background, and an outstanding MSc degree in Engineering, Computer Science, Physics, Applied Mathematics, or a related field. You should be proficient in or willing to learn generative deep learning – in
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technology transfer activities Profile PhD in microengineering, electrical engineering, mechanical engineering, applied physics, materials science, or a closely related field Strong hands-on experience
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collaboration with the team. Your profile PhD degree in physics, materials science, chemistry, electrical engineering or a related discipline Knowledge of UHV and thin-film technology Prior experience in PVD