263 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "University of London" uni jobs in Switzerland
Sort by
Refine Your Search
-
Listed
-
Employer
- ETH Zurich
- University of Basel
- Nature Careers
- ETH Zürich
- Empa
- University of Zurich
- ETH
- HES-SO Genève
- CERN
- EPFL
- EPFL - Ecole Polytechnique Fédérale de Lausanne
- Graduate Institute of International and Development Studies, Geneva;
- Paul Scherrer Institut Villigen
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL
- 4 more »
- « less
-
Field
-
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
-
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
-
. 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
-
systems consisting of individually controlled atoms and atomic ensembles. Current research directions include: Demonstrating scalable architectures for fault-tolerant quantum information processing
-
& 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
-
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
-
plants and investigating the impacts on the market/security. For this purpose, we will use data sourced from the ENTSO-E transparency platform and processed at the RRE. This upgrade will be utilized
-
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
-
further involves supporting the generation of high-quality, reproducible data suitable for intellectual property (IP) development, regulatory pathways and potential commercial use. The technician will work
-
challenges. The work is conducted at the interface of mechanics, artificial intelligence, and computational science. The developed methods will be validated on benchmark problems and real-world data and