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methodologies for the characterization, modelling, and simulation of energy materials Contribute to the FAIR-ification of data, establishing systematic data collection and metadata extraction protocols to enhance
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hydrogen energy sector. Your Job: Conduct cutting-edge research to develop and validate advanced AI methodologies for the characterization, modeling, and simulation of energy materials. Develop code and
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H2 profiles in collaboration with our modeling department Your Profile: MSc in meteorology, physics, chemistry, environmental sciences or a related field with good final grade (German system equivalent
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designs in terms of their techno-economic characteristics an optimization model is to be developed in the institutes’ inhouse modelling framework FINE (https://github.com/FZJ-IEK3-VSA/FINE) in
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Your Job: In this position, you will be an active part of our ESDE research group and the HClimRep consortium. You will contribute to the development of the HClimRep foundation model for climate
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, software developers and domain scientists on, e.g., Development and adaption of existing data mining and machine learning models Design and implementation of machine learning algorithm for extracting
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for self-supervised learning in science Engage in national and international ML/DL communities, most importantly the Helmholtz Foundation Model Initiative Present research results at scientific meeting
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the functional-structural plant model CPlantBox to include mycorrhization, growth of extraradical hyphae in soil, and water flow and nutrient transfer in the soil-plant-mycorrhiza system. Model parameterisation
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membranes. You will develop theoretical models and employ computational methods to describe the coupling between ionic nanofluidics, electric double-layer charging, and interfacial reactivity in electrolyte
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on refining the ETHOS.MIDAS model developed in the last two years. You will identify and quantify new measures for decarbonization and adaptation to climate change. You will refine the existing model and expand