Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- University of Oslo
- University of Bergen
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
- UiT The Arctic University of Norway
- University of Agder
- University of South-Eastern Norway
- University of Stavanger
- Western Norway University of Applied Sciences
- NTNU
- 1 more »
- « less
-
Field
-
master degree within preferentially reservoir engineering, or within applied mathematics, computational engineering, scientific machine learning, preferably acquired recently; or who possess corresponding
-
to high-performance computing facilities and datasets from laboratory experiments will be provided to support simulation and verification of the resulting model. Replicate and learn a theoretical model for
-
Informatics and edge intelligence etc. Must have documented significant Knowledge/Research Background, or Must be able to demonstrate skills on Data Analytics and Machine Learning, in particular on distributed
-
or game theoretic analysis. Experience with large language models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. The evaluation of applicants
-
factories and warehouses with autonomous components. It addresses a fundamental challenge in industrial digitalization: the lack of formal, machine-interpretable representations that integrate structural
-
. The objective is to further develop and validate machine-learning surrogate models derived from high-fidelity multiphysics simulations of reactor transients and quantify how surrogate uncertainties propagate
-
convergence of high-performance computing (HPC) and AI, which is a subject that sees an increasing importance due to the widespread use of AI and in particular machine learning (ML). As today’s mainstream AI/ML
-
focuses on detecting underwater acoustics using AI methodologies. Additionally, CFD simulations combined with physics-informed machine learning will also be examined. Several research and industrial
-
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
-
machine learning is an advantage but is not required. Experience with the design and implementation of survey-experiments is an advantage but not a requirement. Alongside developing their own research ideas