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. This exploitation of the multilayered structure of the XS-Graphs will lead to a very efficient planning and control approach to outperform SOTA solutions. The overall approach will be tested and assessed in
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, as well as designing, testing, and debugging to improve software quality across domains such as FinTech, energy, and Industry 4.0. Within this context, the PhD will contribute to the group’s growing
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research program that brings together physics, chemistry, and machine learning. Your research tasks will include: Uncertainty Estimation in Deep Neural Networks for MLFFs Implement and test uncertainty-aware
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Networks for MLFFs Implement and test uncertainty-aware loss functions Study calibration and post-calibration for predictive uncertainty Integrate uncertainty modules into MLFF architectures Detecting
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. Finally, the research will develop efficient algorithms and test them on realistic networks and using real data from energy and public transport operators. The Doctoral student is also expected
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along with physiological and behavioural data of test drivers and build explainable predictive models out of it. The research team spams Luxembourg, Europe and the USA and will make use of world-leading
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of behavioral paradigms for assessing social learning processes in parent-child interactions; development and programming of survey instruments; recruitment, coordination and implementation of test sessions in