256 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "The University of Gothenburg" scholarships in Norway
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- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
- University of Oslo
- Norwegian University of Life Sciences (NMBU)
- University of Stavanger
- UiT The Arctic University of Norway
- OsloMet
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- BI Norwegian Business School
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- CICERO Center for International Climate Research
- NORWEGIAN UNIVERSITY OF SCIENCE & TECHNOLOGY - NTNU
- Nansen Environmental and Remote Sensing Center
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- OsloMet – Oslo Metropolitan University
- University of Oslo;
- Østfold University College
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in Trondheim. At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here. ... (Video
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. At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here. ... (Video unable to load
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information can be found here: https://www.ntnu.edu/mai Duties of the position Complete the doctoral education until obtaining a doctorate Carry out research of good quality within the framework described above
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information can be found here: https://www.ntnu.edu/mai . Duties of the position Complete the doctoral education until obtaining a doctorate Carry out research of good quality within the framework described
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candidates to conduct research in the field of evidence synthesis. The projects will focus on the development of statistical methods for the synthesis of complex data. About the research group The new research
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systems. By combining microclimate modelling, remote sensing data, and data-driven methods, the results are integrated into a Digital Twin framework. The research will support predictive risk assessment and
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of maintenance. The project aims to develop an integrated decision-support framework that combines inspection images, sensor data, and engineering interpretation to enable more transparent, evidence-based
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three-year PhD–positions related to use of AI for mapping of forest ecosystems. New sensors and increased digitalization generate vast quantities of data that together with advanced statistical methods
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, monitoring even the most basic snow characteristics, such as presence of snow cover or snow depth, remains challenging. Most available satellite-based and modelled snow data products, especially for snow depth
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, GenAI models act as probabilistic black boxes, often producing plausible but factually incorrect information without source attribution. This poses unacceptable risks particularly in high-stakes domains