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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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, Reliability and Trust (SnT) at the University of Luxembourg is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services. We play an instrumental role
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engineering challenge. Key to success will be the development of cost competitive and reliable methods to produce hydrogen via electrolysis of water/steam driven by green electricity. Hydrogen can be used as a
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research. For the working group “Past and Future Earth” (PATH) within the Research Department “Earth System Analysis”, PIK is offering a PhD position (m/f/d) (Position number: 21-2025 PhD PATH) in the field
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noise from other mechanical components such as gears, screws, etc., fault diagnosis using such signals is not an easy task. Having a robust and reliable CM system for low-speed bearings will have
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Reliability and Durability to carry out research in the area(s) of photovoltaic (PV) module reliability and durability, PV system data analysis, and material characterization. This is a 12-month, non-tenure
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, Reliability and Trust (SnT) at the University of Luxembourg is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services. We play an instrumental role
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metrics during both standard operation (primarily governed by system reliability) and extreme events (primarily governed by robustness and restoration). This will be achieved by building on previous
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deep learning methods to enhance the predictions beyond existing data. By incorporating microstructural features into predictive models, the aim is to create a reliable data-driven modelling framework
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networks by analyzing their dynamical systems and probabilistic asymptotic behavior, improving and generalizing diffusion-based generative AI using insights from numerical and stochastic analysis, and making