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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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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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) for scientific and large-scale datasets Implement parallel ML training on the High Performance Computers, including JUPITER, Europe`s first exascale computer Prepare, process and publish datasets and benchmarks
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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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and project meetings as part of your work Opportunity to gather industry-relevant knowledge about the complete manufacturing process of batteries as part of their scientific work - from the synthesis
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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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, physics or a related field of studies. Independent, analytical, and conscientious way of working An interest in system analysis Knowledge about modeling is an advantage. Knowledge about programming
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presenting the results, while collaborating with other group members and collaborators You will obtain your PhD from Bonn University. Your Profile: Completed Master`s degree in physics (or related fields
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reports Mentor students and contribute to teaching at RWTH Aachen Your Profile: Completed Master’s degree in chemistry, physics or related fields Previous eperience with magnetic resonance required