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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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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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systems using various tools and models, including: i) characterization of the emerging patterns in physical systems (solid state materials and active systems); ii) investigation of the mechanical properties
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, C/C++ and/or Java, etc.; experience with the implementation of specialized transport modelling software, optimization algorithms and procedures; strong ability and desire to learn new programming
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for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks