226 computer-science-intern-"https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" positions at ETH Zurich in Switzerland
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interest in data-intensive systems. You bring a solid foundation in software or data engineering, typically developed through a Master’s degree or higher (e.g. PhD) in Computer Science or a related field, or
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, neuroscientists, computer scientists, clinicians, and data scientists across the Singapore-ETH Centre (SEC), the National University of Singapore (NUS), and Nanyang Technological University (NTU), the PhD student
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of Bern). The position will be hosted at the Institute for Atmospheric and Climate Science at ETH Zurich and will be part of the NCCR CLIM+ programme which is funded by the Swiss National Science
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or mechanical engineering, or CS Solid knowledge of computer vision and ML, particularly anomaly detection methods Experience with multimodal data (e.g., image + time series, sensor fusion) is a strong ad-vantage
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Required Experience: PhD in Geodesy, Geomatics, Aerospace Engineering, Signal Processing, or a related field Proven experience in GNSS data analysis and processing Very good programming skills (e.g., Python
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collaboration with various stakeholders (engineering companies, Swiss Armed Forces, armasuisse, international collaborators) to ensure efficient roll-out of the system Profile MSc degree in Geophysics, Seismology
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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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development. Job description We are searching for a scientific assistant who assists and coordinates teaching activities in the international master program Spatial Development and Infrastructure Systems
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, Computer Science, Applied Mathematics, or a field focused on the intersection of Machine Learning and Optimization Proven expertise in surrogate modelling, specifically in designing neural architectures
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of emerging technologies balanced against their dark sides. Our research at the intersection of data, behavioural, and computer science draws on data from real-world and experimental settings The Centre for AI