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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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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training
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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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: Master’s degree in mechanical engineering, process engineering, chemical engineering, energy technology, or a related field Proven expertise in modeling and simulation; familiarity with CFD tools such as
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can be tailored through phase composition, distribution and morphology by tuning process parameters. The work is carried out within the DFG Priority Programme “DaMic - Data-driven Alloy and
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, process engineering, chemical engineering, energy technology, or a related field Proven expertise in modeling and simulation; familiarity with CFD tools such as COMSOL, Ansys Fluent, or OpenFOAM Excellent
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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
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covering the full spectrum of satellite instrument development: design, manufacturing, characterization, data processing, and scientific analysis Opportunities to present your work at national and
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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