Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- University of Oslo
- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
- University of Bergen
- University of South-Eastern Norway
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- UiT The Arctic University of Norway
- University of Agder
- BI Norwegian Business School
- Simula UiB
- University of Stavanger
- 1 more »
- « less
-
Field
-
. Integreat develops theories, methods, models, and algorithms that combine data with general or domain-specific knowledge, helping lay the foundations for the next generation of machine learning. Integreat
-
artificial intelligence (AI) and an increasingly important force in a digital and data-driven world. Integreat develops theories, methods, models, and algorithms that combine data with general or domain
-
National Lab, University of Tokyo etc.), the PhD candidate is expected to research on some of the following themes: New algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient
-
science can be eligible if they possess significant, demonstrable experience with optimization using physics-inspired algorithms or quantum computing frameworks. The applicant must have submitted his/her
-
background in computer science can also be eligible if they possess significant, demonstrable experience with optimization using physics-inspired algorithms or quantum computing frameworks. The applicant must
-
activities as required. Implementing and testing algorithms in high-performing open-source scientific software. Be prepared for changes to your work duties after employment. Required selection criteria You
-
algorithms is anticipated in mainland Norway, Svalbard, and abroad. Funding is also available for conference attendances and research visits with external collaborators. The position is part of the ERC-funded
-
effectively exploited, possibly using some kind of machine learning algorithm, provides more accurate data than traditional data collection methods, e.g. paper-based surveys. This data is valuable to several
-
that integrate prediction and control algorithms, optimizing data transformations, offloading and distributed computing, and exploiting mechanisms such as network slicing and multi-access edge computing
-
algorithm that interface with interpolated geotechnical fields to generate adaptive, variable-length stope geometries. Calibrate and validate the developed models using real-world mine data Develop, document