Sort by
Refine Your Search
-
Listed
-
Program
-
Employer
- University of Oslo
- University of Bergen
- University of South-Eastern Norway
- University of Agder
- UiT The Arctic University of Norway
- NTNU - Norwegian University of Science and Technology
- Western Norway University of Applied Sciences
- Norwegian University of Life Sciences (NMBU)
- OsloMet
- University of Inland Norway
- ; University of Oxford
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- Nature Careers
- Norwegian Center for Violence and Trauma Stress Studies
- Norwegian Institute of International Affairs
- University of Stavanger
- 6 more »
- « less
-
Field
-
develops efficient and robust algorithms for realistic settings in terms of data and computing resources and collaborates to address major challenges in important applications including marine domain and
-
for enjoying the real world. The candidate further develops efficient and robust algorithms for realistic settings in terms of data and computing resources and collaborates to address major challenges in
-
. This insight opens the door for enjoying the real world. The candidate further develops efficient and robust algorithms for realistic settings in terms of data and computing resources and collaborates to address
-
technology management, or smart grids. Experience in development of mathematical meta-models, control strategies, optimization methods and algorithms, data analysis and machine learning techniques, techno
-
technological progress in our increasingly digital, data-driven world. Researchers in Integreat develop theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By
-
particular to the development and validation of novel computational language models, algorithms, and tools for spoken language-based cognitive tests for low-resource languages, and their integration with
-
collaborative skills. Applicants must be proficient in both written and oral English. Experience from one or several of the following areas is an advantage: Developing algorithms for CFD solvers (e.g. OpenFOAM
-
advantage: Developing algorithms for CFD solvers (e.g. OpenFOAM). Programming in C++ or Fortran and proficiency with MATLAB or Python scripting. Experience with tools for simulating chemical kinetic, e.g
-
/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines for plausible narratives of regional climate change, novel algorithms for rare
-
for plausible narratives of regional climate change, novel algorithms for rare event sampling or ensemble boosting, and the development and use of hybrid climate models combining physics-based and ML components