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CS Practical knowledge of RL and IL (e.g., behavioral cloning, inverse RL, dealing with distribution shifts) with applications in (industrial) robotics Hands-on experience with robotics or other
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during field measurements, capture high-resolution imagery of cloud droplets and ice crystals to determine their size distributions and types. The resulting large datasets (often several terabytes
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component of solid-state transformers (SSTs). Such SSTs are required, for example, in future AI data centres, where power consumption per computer rack increases to levels of several hundred kilowatts or even
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workflow Developing seismic monitoring strategies for CO₂ injection, including the design and analysis of surface-based and borehole distributed acoustic sensing (DAS) measurements in preparation
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) distributional generalization, transfer learning, causality Multi-objective settings and alignment, RL theory Statistical learning theory, optimization (e.g., implicit bias) Robustness (broadly defined), privacy
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energy transfer, developing and employing computer simulations, laboratory experiments, and field analyses. Our aim is to gain fundamental insights and develop sustainable technologies to address societal
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electronic switches and converters are increasingly connected to the power distribution system. Their high-switching frequencies and steep voltage slew rates pose a new stress to the insulation of the electric
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The power system is changing, largely driven by the energy transition and climate change. The large shares of renewables, both centralized and distributed, are posing new challenges to system
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. This position offers the exciting opportunity to join the NorSCAPE project, which aims to disentangle the physiological mechanisms underpinning resilience and vulnerability across its distribution range. In
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to work with leading experts across Europe to develop solutions for Decentralised Critical Infrastructure Asset Monitoring and Condition Assessment . This position focuses on next-generation distributed