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learning, non-Hermitian systems The Quantum AI lab at ETH (Prof. Juan Carrasquilla ) invites applications for PhD positions to work at the intersection of computational quantum many-body physics, machine
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD Position in Energy-Efficient Machine Learning for Wearable and Augmented Reality
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optimization – with rigorous theoretical analysis. The ideal candidate has strong machine learning and AI expertise and is comfortable with – or eager to learn – large-scale multi-GPU experimentation
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Your profile PhD applicants must hold a Master's degree in computer science, mathematics, or electrical engineering, with demonstrated strength in either practical implementation or theoretical foundations. Candidates should possess an exceptional academic record and a strong mathematical...
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library. Strong interest in machine learning, reinforcement learning, and fluid dynamics. Ability to work independently and collaboratively in an interdisciplinary team. Excellent command of English, both
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of Zurich and Wageningen University & Research. The four-year STEPS project focusses on developing data-driven and machine learning methods to monitor CO2 and NOx emissions using the upcoming satellite
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machine learning methods to monitor CO2 and NOx emissions using the upcoming satellite missions (e.g., CO2M, TANGO, Sentinel-4/5). Your research will contribute directly to monitoring global efforts
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solution for through-thickness reinforcement of FRPs. Your tasks You will work in collaboration with a postdoctoral researcher/scientist mainly on: Design and manufacturing of SMA Z-pinned FRPs SMA
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doctoral and postdoctoral researchers on material surface development, in vivo small-animal studies, in vitro organotypic bone models and in vivo large animal. The research will be conducted collaboratively
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changes in station or vehicle configurations on travel behavior. The doctoral student will further estimate causal effects through predictive machine learning models, and develop a generalizable decision