17 machine-learning-"https:" "https:" "https:" Fellowship positions at University of Oslo in Norway
Sort by
Refine Your Search
-
PhD Research Fellow in ML-assisted reservoir characterization/modelling for CO2 storage (ref 290702)
-build ups in potential multi-site storage licenses. The research will help to suggest best practices for machine learning integration in de-risking CO2 storage sites. We seek a candidate with a strong
-
-fellow-in-deep-learning-for-subsurface-imaging Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/290391/postdoctoral-research-fel… Requirements Research FieldComputer scienceEducation
-
(PDE). Examples of models in the scope of the project include particle models, stochastic PDE and models from fluid dynamics and machine learning. Place of work is the Department of Mathematics, Blindern
-
, friendly and inspiring, and the position represents a unique opportunity for career development for a hard-working candidate. Main responsibilities Develop and apply machine learning and statistical modeling
-
/english/research/groups/dsb/index.html) as part of Visual Intelligence (http://visual-intelligence.no) , Norway's leading research centre in deep learning for image analysis. Starting date as soon as
-
of Anomalies ” (SODA), newly funded by the Norwegian Research Council and affiliated with Integreat – the Norwegian Centre for Knowledge-driven Machine Learning. We are looking for a motivated candidate, who
-
. • Experience with machine learning and artificial intelligence. • Strong programming skills (e.g., Python, C++), and familiarity with ROS or similar frameworks. • Experience with simulation tools like
-
particle models, stochastic PDE and models from fluid dynamics and machine learning. What skills are important in this role? Qualification requirements: The Faculty of Mathematics and Natural Sciences has a
-
-working candidate. Main responsibilities Develop and apply machine learning and statistical modeling techniques, including novel AI architectures, for the analysis of complex traits and precision prediction
-
pressure-build ups in potential multi-site storage licenses. The research will help to suggest best practices for machine learning integration in de-risking CO2 storage sites. We seek a candidate with a