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develop advanced models, algorithms, and control solutions for simulating, optimizing, and operating future integrated energy systems. We address the challenges arising from the increasing integration
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-of-the-art multi-scale electrophysiological data using modern analysis techniques construct data-informed stochastic and deterministic simulations of activity data to model observed dynamics and to assess
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planned for the next decade About your role: understanding and identifying the electromechanical accelerating cavity system designing, simulating, implementing, and testing novel system identification
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across the world. We are looking for motivated researchers to join the HDSC/MDSI Tandem Project "Data science at scale: Training neural models with mixed precision solvers." This is a joint project
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past datasets within the URBank (https://urbank.earth ) initiative. The full-time position will be for a period of 3 years, with the possibility of a 1-year extension, and based in Jena, Germany. The
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The project unites systems chemistry, advanced microfabrication and quantitative microscopy. It also offers the chance to develop simulation approaches for reaction–diffusion processes to rationalize and
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) we investigate the chemistry and transport of trace substances in the troposphere. To this end, field and simulation chamber experiments as well as model simulations are conducted. Your Job The current
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assisted ISAC framework available at the TUM ACES Lab. ▪ Conducting real world experiments to analyze performance deviations between theoretical models, software simulations, and hardware behavior. ▪ Working
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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embedded in the Bremen Research Cluster for Dynamics in Logistics (LogDynamics) (http://www.logdynamics.de ). LogDynamics is a cooperative network of research groups from five different departments