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the complete chain from materials properties to process design and evaluation. More information on the project can be found here: This specific project (DC7) addresses membrane adsorbers, which have small
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networks using the numerical simulations Calculating the likelihood of material parameters correctly describing experimental results Correlating material parameters with process conditions of sample
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H2 profiles in collaboration with our modeling department Your Profile: MSc in meteorology, physics, chemistry, environmental sciences or a related field with good final grade (German system equivalent
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information from experiment data Evaluation and development of specialized and/or interpretable machine learning approaches for the domain of materials science, physics, microscopy Incorporation of machine
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the overall design Your Profile: Master’s degree in the field of natural sciences, engineering, or a related field of study Knowledge of energy process engineering is an advantage Huge interest in energy
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to educational events, such as university lectures, JSC courses and hackathons. Your Profile: Excellent Master in Computer Science, Mathematics, or Physical Science, Very good knowledge and proven skills with
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in physics, physical chemistry, chemical engineering, or related field Knowledge and competences in physical theory and modelling Experience and interest in computer programming Basic knowledge
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membrane electrode assemblies (MEAs) for electrochemical characterization Physical, spectroscopic, and electrochemical characterization of MEAs prior to, during, and after operation Participation in project
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well as in fuel cells. Your tasks in detail: Manufacturing of catalyst layers and membrane electrode assemblies (MEAs) for electrochemical characterization Physical, spectroscopic, and electrochemical
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science, physics or a comparable field of study Background knowledge and proven interest in the field of electrochemistry Fluent in spoken and written German as well as English Willingness to get familiar