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universities. The transition towards electrified and energy-efficient energy systems poses significant challenges in predicting the coupled behaviour of thermofluid, electromagnetic, and rotordynamic processes
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are software representations of physical assets, processes, or systems. They leverage real-time data to mirror the behaviour and characteristics of their physical counterparts, enabling predictive maintenance
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reliably assess fatigue damage and predict remaining lifetime, enabling proactive maintenance strategies that extend service life and reduce CO2 emissions. Through this PhD scholarship, you will contribute
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reliably assess fatigue damage and predict remaining lifetime, enabling proactive maintenance strategies that extend service life and reduce CO2 emissions. Through this PhD scholarship, you will contribute
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artificial intelligence and machine learning for clinical decision-support systems and disease prediction modeling to join our research group. The successful candidate will develop and apply advanced AI
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leads to inefficient operating points, poor maintenance causes equipment degradation and energy waste, and sloppy or absent commissioning results in poor system balance and underutilized energy-saving