Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- University of Stavanger
- NTNU - Norwegian University of Science and Technology
- UiT The Arctic University of Norway
- University of Oslo
- NTNU Norwegian University of Science and Technology
- University of South-Eastern Norway
- University of Bergen
- Simula Metropolitan Center for Digital Engineering
- University of Agder (UiA)
- BI Norwegian Business School
- Molde University College
- Nord University
- SINTEF
- University of Agder
- Western Norway University of Applied Sciences
- 5 more »
- « less
-
Field
-
they support running optimally under any given circumstances. Artificial intelligence in networking, i.e., applications of AI to improve network performance, maintainability, security, automated repair and other
-
structured around two main pillars: Network resilience and sovereignty, i.e., research on networking architectures and mechanisms that keep critical networks and applications they support running optimally
-
discovery, engineering optimization, energy systems, large-scale AI, and cybersecurity. Defining operational and design principles for quantum computing architectures is crucial to enable realistic
-
AI models into complex industrial and urban environments. Expertise in designing distributed architectures for IoT, edge, and cloud. Proficiency in handling large-scale data and optimizing information
-
opportunities in combining physical meteorological/hydrological/hydraulic modelling and AI methods to improve the inflow prediction, and optimize the control of and use of water in the water courses considering
-
. The position is part of a small team that works on the development and optimization of algorithms for these problems, as well as proofs on theoretical complexity bounds. Common tasks include: Developing ideas
-
future direction (max 3 pages). A narrative teaching portfolio outlining prior teaching experience and pedagogic qualifications (max 3 pages; optimally organized around the four principles of Scholarship
-
in crystalline rocks. Drilling optimization using machine learning, e.g. predicting rate of penetration (ROP) and wear. Investigate the possibilities in automation and robotization and the use
-
stresses, drill wear (bit replacement). Collect and analyze existing and new data on drillability related to mineral content and degree of metamorphosis in crystalline rocks. Drilling optimization using
-
storage in depleted petroleum fields: In this project, you will investigate and aim to optimize CO2-assisted oil production (CO2EOR) and subsequent CO2 storage. Simplistic models for a fundamental