Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- University of Oslo
- University of Bergen
- NTNU - Norwegian University of Science and Technology
- University of South-Eastern Norway
- Western Norway University of Applied Sciences
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- UiT The Arctic University of Norway
- University of Agder
- University of Stavanger
-
Field
-
. 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 important applications including
-
. 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
-
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
-
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
-
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
-
of the project is to develop new conceptualizations of what AI means to people, grounded in everyday life, and to assess democratic implications of emergent media technologies. The postdoc will design and conduct
-
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
-
intelligence (AI), and analyses changes in trust and understandings, at a time when AI becomes integrated into everyday media experiences. The goal of the project is to develop new conceptualizations of what AI
-
/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