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operation Participation in project meetings. Coordination with internal and external partners Publication and presentation of research results in relevant journals and at international conferences Your
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of structure-performance relationships of different catalyst and membrane surfaces Development and validation of various test stand modifications Coordination with internal and external project partners from
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computing to develop a continuous and local alternative to existing gradient-based learning rules, bridging theories of predictive coding with event-based control/ Simulate models of the learning algorithm
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collaboration with a team of experts at FZJ (INM-9: Institute of Neuroscience and Medicine - Computational Biomedicine, IBI-1: Institute of Biological Information Processing - Molecular and Cellular Physiology
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on materials science tasks as well as integrate your semantic-AI services into high-throughput GPU/HPC workflows, contributing to data management, metadata structuring, and semantic annotation Collaborate with
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outstanding key performance indicators. Your tasks in detail: Contribute to develop an innovative process for the autothermal dehydrogenation of LOHC using fundamental knowledge of chemical engineering
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of the regions of interest determined in X-ray imaging for later analysis using high-resolution transmission electron microscopy and atom probe tomography Performing transmission electron microscopy experiments
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Your Job: Join the Safe.SIB research project to develop new battery cell chemistries or introduce new functionalities into an existing battery technology to achieve safe, ultra-high performant
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activity (work, studies, etc.) in Germany for more than 12 months in the last 36 months Master’s degree in physics, electrical/electronic engineering, computer science, mathematics, or a related field
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energy system model workflows Your Profile: Master’s degree in computer science, data science, natural sciences, economics, engineering, mathematics or a related field of study Huge interest in data