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, long-term usability, and building systems that are secure by design and aligned with best practices. You likely have a background in software engineering, data engineering, or a related field, and an
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understanding of the aging of solid insulation under mixed-frequency medium-voltage stress, see https://doi.org/10.1088/1361-6463/acd55f for a relevant example research work of our team in this area. Profile
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The current era of artificial intelligence is predominantly driven by advances in computational power and infrastructure. As models scale to unprecedented sizes, their capabilities are enhanced
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capabilities, with only a very small fraction dedicated to AI safety. The research of the new assistant professor tenure track (APTT) will focus on the mathematics of responsible and trustworthy AI
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are FAIR-compliant, secure, efficient, and operated in a sustainable manner over the long term. Furthermore, you will actively participate in the design, prototyping, and further development of RDM
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deployment by enabling quick data collection, calibration, and policy training while ensuring safety and efficiency. For this, we develop novel learning-based control and policy optimization techniques. We're
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, safety, and societal acceptance of CO₂ injection into Switzerland's subsurface. Time-lapse seismic-reflection imaging is the primary tool to image the CO₂ plume and its migration through the subsurface
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close collaboration with experimental and clinical partners, and we are embedded in one of the strongest AI and computer science environments worldwide. Learn more about our research and recent work
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service providers operating in high-demand sectors including offshore wind, oil and gas, but also maritime security, demining (UXO, MCM) and search & rescue. As an ETH Zürich Spin-Off, we are a fast-growing
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-facturing processes. In this internship, you will work on state-of-the-art anomaly detection methods using computer vision and time-series data, with a particular focus on multimodal data fusion for powder