216 computer-science-intern-"https:"-"https:"-"https:"-"https:"-"University-of-Hull" positions at ETH Zurich
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, Switzerland [map ] Subject Areas: Computer Science / Distributed Systems and Networking , Networking , Networking and distributed systems Appl Deadline: 2026/01/08 11:59PM (posted 2025/11/10, listed until
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Contribute to supervision of students and interns where appropriate Profile PhD in climate science, atmospheric science, computer science, data science, physics, applied mathematics, remote sensing, or a
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the candidate must be able to fluently communicate in English (oral and written) and be willing to work in a highly interactive, international team candidates must hold a MSc degree in Molecular Sciences, Life
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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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The Strategic Communication Officer plays a central role in shaping NCCR Catalysis's positioning, both internally and externally. Working at the interface of science, strategy, and communication
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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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, 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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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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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