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-economic metabolism, for example with material flow analysis or life cycle assessment Deep understanding of engineering principles for evaluating novel technologies and provide guidance for their ecological
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. You will perform microstructural characterization of dry coated electrodes using physical and machine learning based methods and the electrochemical assessment of the electrodes in battery cells. Your
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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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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