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to facilitate a rapid and efficient exchange among experimental and computational groups and Devise an approach in invertible predictive modelling that links semiconductor properties to the composition of lead
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. You will employ the trypanosome model established in our group to study its swimming behavior in soft tissue-like surroundings. This project is a part of the DFG-SPP 2332 priority program “Physics
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the Institute of Advanced Simulation – Materials Data Science and Informatics (IAS-9) and the Institute of Energy Materials and Devices – Structure and Function of Materials (IMD-1) to establish a data-driven
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scenarios and strategies for its successful implementation. Using existing ICE-2 energy system models, you will address questions such as: How do installation and retrofitting times impact the restructuring
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with microstructural features and failure mechanisms Development of models to describe degradation mechanisms and predict component lifetime Presentation of research findings at project meetings
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) engineering, computer science, or a related field You have very good knowledge of energy technology, energy economics, and energy policy You have already gained initial experience in energy system modeling You
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Your Job: Analysis of possible applications of CO2 capture and CO2 utilization in the energy system requires detailed techno-economic modelling of these technologies. A major focus of this doctoral
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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
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identify the necessary solutions. The findings should also remain valid under different climate change scenarios. With its global state-of-the-art energy system model, ICE-2 at Forschungszentrum Jülich
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support the development of a European energy system model by benchmarking future technologies and optimizing their representation within the FINE optimization modelling framework ( https://github.com/FZJ