Sort by
Refine Your Search
-
Listed
-
Employer
- University of Oslo
- University of Bergen
- NTNU Norwegian University of Science and Technology
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- NTNU - Norwegian University of Science and Technology
- UiT The Arctic University of Norway
- University of South-Eastern Norway
- Western Norway University of Applied Sciences
-
Field
-
of Oslo’s Department of Informatics (IFI) and is hosted by the Network and Distributed Systems Research Group (ND) with co-supervision from IFI’s Machine Learning section and the University of Inland
-
to demonstrate skills on Data Analytics and Machine Learning, in particular on distributed ML. Must have very good programming competence in Python, Java, C/C++ or equivalent Fluent oral and written communication
-
is part of the ERC-funded project “Actively learning experimental de-signs in terrestrial climate science (ACTIVATE)”: https://www.mn.uio.no/geo/english/research/projects/activate/index.html The PhD
-
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
-
Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field observations—including
-
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 project is supervised by Associate Professor Ulysse Côté-Allard at the Department of Technology Systems, University of Oslo, whose research focuses on the development of machine learning algorithms
-
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
-
. 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
-
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