Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- University of Oslo
- NTNU - Norwegian University of Science and Technology
- University of Stavanger
- University of Bergen
- Norwegian University of Life Sciences (NMBU)
- OsloMet
- University of Agder
- UiT The Arctic University of Norway
- Østfold University College
- ;
- CICERO Center for International Climate Research
- CMI - Chr. Michelsen Institute
- Høgskulen i Volda
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- NTNU Norwegian University of Science and Technology
- Nature Careers
- Nord University
- University of Oxford
- 8 more »
- « less
-
Field
-
participation in the war in Myanmar since the 2021 military coup d’état. This responsibility includes a mapping of the conflict’s digital war ecology and focusing in on a specific example of remote participation
-
Knowledge about energy systems, especially the operational characteristics of renewable energy production (wind/solar) and batteries Knowledge and interest in applying AI/machine learning to time-series data
-
public defence are eligible for appointment Strong programming and artificial intelligence/machine learning skills The candidate’s research proposal must be closely connected to the call and the research
-
), enabling cross-contextual learning and refinement of policy recommendations. A postdoc with expertise in urban built environment studies and qualitative social sciences will play an important role in
-
participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI LEARN’s mission is to establish an internationally leading
-
Fellow should acquire. UiO is responsible for following up on the career plan and ensuring that the Postdoctoral Fellow has access to career guidance throughout the postdoctoral term. If the Postdoctoral
-
methodologies Experience with machine learning techniques Experience with pipeline development and testing (gitlab, simulated light curves…) Ability to work independently and to collaborate in an international
-
solvers, with the goal of exploiting models of various complexity, ranging from high-performance computing, via reduced-order models to data-driven (machine-learned) representations. In particular, we
-
resulting precipitation and extreme weather. We study global and regional climate change and are at the core of international community climate modeling efforts that also involve AI and Machine Learning. We
-
to: compositional multiphase reservoir simulation upscaling or screening methodologies optimization of well positions and control strategies economic assessments machine learning or proxy-model based methods field