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for the automatic generation of future energy technology scenarios based on LLMs, patent data, and scientific literature Investigating the system-level conditions under which future technologies like fusion and
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Description In the research group Dependable and Autonomous Cyber-physical Systems (DACS) on Institute for Software and Systems Engineering (ISSE) at Clausthal University of Technology has
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susceptible steel structures. Thus, the candidate will develop reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be
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Description TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. Founded in 1828, today it is a globally
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of future applications from the fields of structural lightweight construction, energy research and medical technology. The experimental development is closely accompanied by modelling approaches and
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of global trends and technology-specific impacts on electricity production Integration of projected volatile electricity production into existing energy system models along with an assessment of how climate
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grades in the field of mechanical engineering, material science, physics, computational science or similar, preferably with a specialization in the field of theory and/or simulation Strong understanding
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the field of natural or (industrial) engineering, informatics, or a related field of study Very good knowledge of energy technology, energy economics and energy politics Initial experience in energy system
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capture, CO2 utilization and negative emissions options in a greenhouse gas-neutral energy system Your Profile: • Master`s degree in the field of natural sciences, engineering, industrial engineering or a
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technology and economics Experience in energy system modeling is beneficial First programming skills, ideally in Python Independent and analytical way of working Reliable and conscientious working style Fluent