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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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Your Job: Your research will explore the integration of emerging energy technologies into future energy systems and assess their potential contributions toward a greenhouse gas-neutral Europe. A key objective is to identify under what technical and economic conditions innovative...
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degree (or equivalent) in Data Science, Computational Biology, Bioinformatics, Computer Science, Physics or a related field Solid programming skills and knowledge in deep learning, statistical modelling
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physics, microbial ecology, plant nutrition, plant physiology, plant ecology, biochemistry, and/or bioinformatics Strong interest in using process-based mathematical modeling to simulate biogeochemical
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to enhance the understanding of the soil-root system Find links between non-invasive geophysical monitoring and the availability of Nitrate and water in the soil Improve the small-scale process understanding
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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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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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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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-based processing. This project will investigate event-driven learning approaches in the context of RL in an event-triggered fashion. Data efficiency will be improved by using meta-learning and pre