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on investigating the change in the catalysts surface under relevant process conditions using spectroscopic analysis methods. Your task will include: Application of established and novel methods for the preparation
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(STEM) and TEM in order to advance our understanding of vitrified biological specimens. Develop novel methods for the application of cryo-(S)TEM methods to biological specimens including the operation of
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skills and experience with numerical modeling and particle-based methods Interest in working closely with experimentalists Excellent written and spoken English skills Experience with parallel programming
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for the accurate calculation of materials properties. You will combine methods from quantum informatics and solid-state physics to describe the complex electronic and ionic processes in battery materials. New
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analyses Electrode integration and processing studies: Incorporate catalysts into planar electrodes and investigate processing effects to ensure reproducible, high-performance assemblies Electrochemical
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the complex electronic and ionic processes in battery materials. New quantum algorithms will be tested for addressing topical questions in materials development for next-generation batteries. In this project
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practices. Within this framework you will: extend and use a process-based modeling approach which explicitly represents microorganisms and biomolecule functioning in soil systems. use process-based modeling
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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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chemical and physical analyses using XRD, XPS, SEM, TEM and Gas sorption analyses Electrode integration and processing studies: Incorporate catalysts into planar electrodes and investigate processing effects
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methods to make them usable for transparent energy systems analyses. The collected data will be processed and semantically enriched using methods you develop before being transferred to a knowledge graph