Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- University of Oslo
- UiT The Arctic University of Norway
- University of Bergen
- NTNU - Norwegian University of Science and Technology
- University of Stavanger
- OsloMet
- NTNU Norwegian University of Science and Technology
- University of South-Eastern Norway
- CMI - Chr. Michelsen Institute
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- Nansen Environmental and Remote Sensing Center
- Simula Research Laboratory
- Simula UiB
- University of Agder
- Østfold University College
- 5 more »
- « less
-
Field
-
to work on cutting-edge research at the intersection of deep learning and computer systems. The successful candidate will join an international and collaborative research environment and contribute
-
to work on cutting-edge research at the intersection of deep learning and computer systems. The successful candidate will join an international and collaborative research environment and contribute
-
the Department). The position is financed by the University of Bergen. About the project/work tasks The PhD project aims to investigate how methods from Scientific Machine Learning (SciML) can enhance modelling
-
, energy system optimization and possibly machine learning to guide energy transitions towards net-zero systems. The research supervisors have prepared multiple potential projects in this area and will work
-
Signal Processing and Image Analysis group (DSB), Section for Machine Learning, at IFI. DSB has seven full-time and five adjunct positions and carries out research across image analysis and machine
-
, wearable physiological sensing, and machine learning to uncover how factors like fatigue and cognitive workload impact technician performance. Join us to develop predictive models that predict human error
-
with numerical modeling, energy system optimization and possibly machine learning to guide energy transitions towards net-zero systems. The research supervisors have prepared multiple potential projects
-
modelling, or modelling of physical/dynamical systems. familiarity with AI/machine learning/system identification techniques and their application to engineering problems. knowledge of digital twin concepts
-
the use of modern machine‑learning methods within applied mathematics—particularly physics‑informed learning, anomaly detection, data‑driven modelling, and the construction of surrogate models grounded in
-
for Catalysis and Organic Chemistry at the Department of Chemistry. The group has extensive experience in computational modelling, reaction mechanisms, and machine learning for catalyst design and discovery. Nova