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Field
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, reliability, and availability of complex industrial systems while making maintenance strategies more cost-efficient. Together, UESL and IMOS are seeking a motivated and qualified PhD candidate to advance
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railway infrastructure, with an extensive network spanning thousands of miles and its complex web of tracks, station, signalling systems and more, and is fully committed to advancing research and innovation
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modeling with experimental validation and has two major objectives: Development of a physics-informed neural network (PINN) framework You will design and implement a simulation framework to model
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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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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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education in digitalization and electrification including renewable energy sources, electric vehicles, industrial IoT, AI, 6G communication and wireless sensor networks as well as research and education
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modelling, data assimilation, and multi-scale neural network architectures applied to spatio-temporal data. The development of these methods is motivated by a concrete and important application: inferring gas
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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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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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you often find yourself fascinated by the multiscale complexity of real-world engineering problems? In this project you will develop advanced multiphase transport models for porous systems, bridging