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to the FAIR-ification of data, establishing systematic data collection and metadata extraction protocols to enhance machine learning-based software applications for materials science. Your Profile: Master’s
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and develops a wide range of topics related to chemical hydrogen storage along the entire process chain. We place a particular emphasis on LOHC technology, addressing issues across different scales. Our
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machine learning-based software applications for materials science Develop code and utilize machine learning to support the automation of characterization and fabrication processes Ensure the integration
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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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preparation Deriving insights for film and device fabrication based on the above described workflow Your Profile: Masters degree in physics, materials science, physical chemistry, electrical engineering and
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Your Job: Nanoporous materials are key components in electrochemical hydrogen technologies, most importantly in the form of electrocatalytic layers (ECLs) of fuel cells and electrolysers
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Your Job: The electrocatalytic interface engineering department led by Prof. Dr.-Ing. Simon Thiele focuses on synthesis, manufacturing, analysis and simulation of functional materials to find
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Profile: Master’s degree in Materials Sciences, Chemistry, Physics, Radiochemistry or a related discipline Experience or interest in nuclear energy, waste management, nuclear fuel or spent nuclear fuel
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interface You take participance in regular project meetings with the involved partners from industry and research Your Profile: Completed academic studies (Masters) in chemistry or materials science or a
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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 (DC2) addresses preparative scale cross flow filtration