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Field
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OR and ML methods and enable robust, transparent, and fair solutions that reflect user preferences and address complex, uncertain real-world scenarios. This PhD position belongs to the work package WP6
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for robust, availability-driven asset management in high-voltage transmission systems. The successful candidate will investigate failure modes, maintenance philosophies, condition-monitoring requirements, and
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is therefore necessary to get a better grip on these interactions, but also to make sure that uncertainties are propagated in a sound and robust way. The envisaged PhD candidate shall investigate
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community built on collaboration, innovation, creativity, and belonging. Our collective success depends on the robust exchange of ideas—an exchange that is best when the rich diversity of our perspectives
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radar setups like the PARSAX and MESEWI systems, and state-of-the-art equipment for indoor and outdoor sensing experiments. This robust infrastructure enables students and researchers to pioneer solutions
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effective, socially robust, and relevant to real-life residential contexts. The project employs a combination of methods, including controlled experiments in the IHBI laboratory, studies in immersive and
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safety functions directly into the system design, ensuring stable and robust operation without compromising overall efficiency. A central component of the project is the design, implementation, and
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AI systems for real-time 3D mapping on compact, low-power devices. The project will combine optical sensing, event-based vision, and radio-frequency (RF) data with advanced AI to build robust mapping
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. Characterizing the developed components Developing robust methodologies to feedback experimental characterization results into the design and layout flow Creating compact modeling methods to incorporate
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prototype into a formalized, robust, and generalizable scientific architecture. First, the PhD candidate will consolidate and extend the structuring of heritage data by developing an operational ontology