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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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the standardization and harmonization of data across platforms. Work with large language models (LLM) and deep learning algorithms to drive the inverse design of materials and uncover new physical and chemical
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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