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building sensor data, you will train AI models to recognize abnormal performance patterns, quantify quality-adjusted service life, and autonomously recommend whether a system needs maintenance, recalibration
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, you will train AI models to recognize abnormal performance patterns, quantify quality-adjusted service life, and autonomously recommend whether a system needs maintenance, recalibration, or a capacity
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and digital transitions, often referred to as the twin transitions. Across Europe, governments, industries and public institutions increasingly rely on scenarios, models, indicators, roadmaps and
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user-informed machine learning models that incorporate geological/geotechnical priors to interpolate 3D rock mass properties between sparse data points. Architect and implement a new stope optimization
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element simulations, blast loading models, propagation of pressure waves in the ground and soil–structure interaction analyses to establish methods for assessing the protective capacity of existing
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infrastructure. The research will combine advanced finite element simulations, blast loading models and fluid–structure interaction analyses to establish methods for assessing the protective capacity of existing
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demonstrate that richer, uncertainty-aware environmental models lead to more capable and efficient autonomous underwater surveys. This PhD project will develop principled methods for fusing these heterogeneous
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on AI-empowered asset management using drone inspections and available asset management historical data. The aim is to enhance the asset and network resilience of ports by training an AI model on ferry
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element simulations, blast loading models, propagation of pressure waves in the ground and soil–structure interaction analyses to establish methods for assessing the protective capacity of existing
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. The project offers opportunities to contribute to research with strong relevance for the decarbonization of industry, while gaining expertise in modelling, analysis, and potentially experimental investigation