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kinetic modeling Experience with (or willingness to learn) 3D modeling, CAD software, and 3D printing What We Offer: TV-L (E13, 67%) collective agreement ( Current TV-L pay table [pdf-Datei] ) A three-year
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. Furthermore, we use the olfactory network as a model to study the dynamics of neuronal development, synaptogenesis, neuronal degeneration, and regeneration. Our research is complemented by behavioral studies
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civil/electrical/control engineering or mathematics or related study programs with a solid basis in choice modelling and/or reinforcement learning, with knowledge of MATSim is advantageous. Description
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phenomena such as the spread of misinformation or the formation of filter bubbles. For this, we rely on rigorous probabilistic methods to model and analyse the intrinsic complexities of these systems
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working atmosphere flexible and family-friendly working time model and the possibility of mobile working (up to 50% of working time) subsidy for a company ticket for public transport
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an exceptional international team with expertise in all aspects of the project. Your tasks will include: • Preparation of different EO and in-situ datasets for training a machine learning model • Development of ML
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://github.com/FZJ-IEK3-VSA/RESKit ). This framework currently uses historical weather data to model energy output and will be further developed to allow the simulation of renewable electricity production under
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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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available on site for the development of suitable radiotracers. One focus of the work is on the use and evaluation of large tomographic data sets to derive parameter data for reactive transport modeling
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phenomena such as the spread of misinformation or the formation of filter bubbles. For this, we rely on rigorous probabilistic methods to model and analyse the intrinsic complexities of these systems