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computational methods using network-based analysis, machine learning and dynamic modeling. We are a young, dynamic team at the idyllic Dahlem campus and teach mainly in the Computer Science, Bioinformatics and
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is the role of clouds in the climate system. For this, we apply and develop a wide range of numerical approaches that cover highly idealized heuristic models to very detailed Lagrangian representations
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computational models with the "exact" but lower resolution information available from experiments. Job description: Application of specially developed approaches to define for transferable force-fields with
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CRC 1114 aims at methodological developments for the modelling and computational simulation of complex processes involving many (more than two) interacting scales, driven by real-life applications
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), and computational modeling (deep neural networks). We apply multivariate analysis methods (machine learning, representational similarity analysis) and encoding models. Job description: This is an open
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computational models with the "exact" but lower resolution information available from experiments. Job description: - Research and teaching is done on statistical physics and machine learning in physics
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theoretical models. We also explore our results directly for applications in terahertz photonics, including the development of novel emitters, detectors and modulators of broadband terahertz radiation. https
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theoretical models. We also explore our results directly for applications in terahertz photonics, including the development of novel emitters, detectors and modulators of broadband terahertz radiation. https
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mechanisms. Job description: • Research and development in the field of AI-supported analysis of biomedical data • Implementation and evaluation of methods for AI validation and model interpretability
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. Numerical models of varying complexity and different observational data sets are used to study atmospheric processes and phenomena on time scales ranging from single weather events to long term climate change