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Field
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theoretical and computational methods to describe electronic, optical, and transport properties of complex materials. The first advertised project (a) is dedicated to the methodological enhancement
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the design of a scalable, interoperable, and resilient quantum internet architecture and protocol stack for real-world operation in hybrid quantum–classical networks across intra- and inter-domain settings
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scope of the PhD position(s) fall within the areas of novel low-complexity neural network architectures, generative audio techniques, and the integration of large language and speech foundation models
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machine learning models to extend their compatibility with problems from mechanical engineering and materials research that could not be addressed so far due to their high complexity, which prevents
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plasticity platform. Different machine learning strategies will be explored to capture the complex relationships between microstructural features and mechanical responses. In particular, the project will
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induced seismicity. Current models remain limited by the scarcity, heterogeneity, and noise of available data, as well as by incomplete knowledge of the subsurface. Physics-Informed Neural Networks (PINNs
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embedded in the Doctoral Programme in Complex Systems Science at the University of Luxembourg. The modelling approaches developed in this project share conceptual similarities with adaptive network and
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. More specifically, the doctoral program has four different specialization tracks: - Complex Systems and Networks (CN) - Computational Mechanics (CM) - Learning and Control (LC) - Software Quality (SQ
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tasks The scope of the PhD position(s) fall within the areas of novel low-complexity neural network architectures, generative audio techniques, and the integration of large language and speech foundation
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operation in hybrid quantum–classical networks across intra- and inter-domain settings. The candidate will study architectural models, including the placement and roles of quantum repeaters, memories, and