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these challenges by advancing sensitivity-based modelling, fluid–structure interaction (FSI) methods, inverse problem solving, and surrogate modeling techniques, ultimately enabling predictive, adaptive, and
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recovery efficiency. Working closely with our experimental collaborators, you will analyse and model experimental data to efficiently explore the large design space of membrane functionalization and
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physics, multiscale modeling, and uncertainty quantification. The Multiscale Modeling of Fluid Materials group at the Technical University of Munich is looking for talented and ambitious scientists
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emphasis is placed on building information modelling, point cloud capturing and processing as well as knowledge representation and inference. In the research project AI-CHECK, new technologies for checking
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of the Collaborative Research Center Transregio 277 Additive Manufacturing in Construction (AMC) in the new project B06: Material Modelling and Simulation of Deposition AM Processes on the Part Scale
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, Metric Learning, Reinforcement Learning, Graph Representation Learning, Generative Models, Domain Adaptation, etc.) for Design Automation applications. To this end, we focus on developing general methods
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: analysis of probabilistic systems (Markov decision processes, stochastic games, chemical reaction networks), automata theory and temporal logic, machine learning in verification, building model checkers
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consumption, and costs Tool and model integration To learn more about our previous work, please check out our website (www.cda.cit.tum.de/research/etcs/ ) and open-source implementations referred therein. Your
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. For an overview of our previous work, please check out our web pages on software/design automation for microfluidics (www.cda.cit.tum.de/research/microfluidics/ ). In the future, we are aiming to extend our
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computers Developing algorithms to decompose (arbitrary) unitaries into native operations of a given target system Optimizing circuits taking error models of actual hardware into account Exploiting quantum