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The candidate should have a PhD degree in Microbiology, Biotechnology, Materials Science or related disciplines with a deep scientific curiosity in bacteria-materials interactions. Key qualifications include
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Apply machine learning techniques for data analysis and time-series forecasting Collaborate in a multidisciplinary team to accelerate functional thin film development Your work will be part of a larger
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willingness to learn A solid foundation in experimental research, data analysis, and scientific methods Interest in machine learning and data-driven approaches to materials discovery Strong interest in hands
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, Matlab, C++) for developing new simulation frameworks or image processing algorithms Experience in or willingness to learn independently operating additive manufacturing systems (DED and LPBF), including
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team members Perform accelerated optical degradation tests of transparent conductive materials Apply machine learning techniques for data analysis and time-series forecasting Collaborate in a
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strong willingness to learn A solid foundation in experimental research, data analysis, and scientific methods Interest in machine learning and data-driven approaches to materials discovery Strong interest
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a multidisciplinary environment, driven by scientific curiosity and open to learning new topics. The Ph.D. candidate needs to be proficient in spoken and written English and have a Master's degree. S
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beam-material interactions Good programming skills (e.g. Python, Matlab, C++) for developing new simulation frameworks or image processing algorithms Experience in or willingness to learn independently