376 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" "UCL" positions in Switzerland
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- ETH Zurich
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- ETH Zürich
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- Paul Scherrer Institut Villigen
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL
- University of Zurich
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- Physikalisch-Meteorologisches Observatorium Davos (PMOD)
- University of Berne, Institute of Cell Biology
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approaches to partition existing forest flux data sets across Europe Identification of flux drivers and their temporal development to understand responses of forests to climate and extreme events Compilation
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will be responsible for a range of research tasks, including literature search, data curation, coding for systematic literature reviews, data analysis, and miscellaneous administrative activities (e.g
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60%-80%, Zurich, fixed-term Are you an ambitious data scientist with strong analytical and numerical skills, and expertise in geomatics, remote sensing, and data processing? We invite you to join
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. Isabel Z. Martínez and is based on rich administrative and survey data from Switzerland. The position offers the opportunity to gain insight into rigorous empirical research and to contribute actively
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on our work and constantly improve it. We believe that the world can be improved with open data. Project background As part of our Open Science team, you will support us in developing efficient data
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systems consisting of individually controlled atoms and atomic ensembles. Current research directions include: Demonstrating scalable architectures for fault-tolerant quantum information processing
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& further information International Networking Close Menu Engagement back Overview PageEngagement Awards Knowledge exchange Action plan 2025-27 back Action plan 2025-27 Empowerment GROW Guidelines & Quality
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Balance (TV/TB) and hardware verification tests Develop and maintain thermal models of spaceborne platforms or thermal control technologies, correlating with test data for validation Establish thermal
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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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data, spanning from whole-genome variant calling and decision support to single-cell sequencing analysis. We develop and extend robust analysis workflows and reusable components, in close collaboration