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opportunity to tackle these two complementary perspectives. In the first direction, you will develop advanced system identification techniques that combine nonlinear dynamics theory with machine learning tools
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new paradigm in the design, testing, and validation of Electronic Control Units (ECUs) for Connected and Autonomous Vehicles. By developing high-fidelity Digital Twins (DTs) that combine functional
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the first direction, you will develop advanced system identification techniques that combine nonlinear dynamics theory with machine learning tools. The goal is to extract governing equations directly from
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particle physics, perform data analysis and develop object reconstruction algorithms in the ATLAS experiment. You will become a member of the ATLAS collaboration and will be based in Nijmegen. Furthermore
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to develop intelligent systems that are both data-efficient and physically consistent. The successful candidate will contribute to advancing novel methodologies that integrate domain knowledge with learning