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methods (such as Machine Learning, Metric Learning, Reinforcement Learning, Graph Representation Learning, Generative Models, Domain Adaptation, etc.) for Design Automation applications. To this end, we
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/model checking, probabilistic or timed systems, automated theorem proving (Isabelle, Coq). Besides, we strongly appreciate experience with developer activities as described below. Developer: Your tasks
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capacity, energy 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
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applications. 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
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on superconducting quantum 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