Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- University of Oslo
- University of Bergen
- University of South-Eastern Norway
- UiT The Arctic University of Norway
- University of Stavanger
- Western Norway University of Applied Sciences
- NTNU - Norwegian University of Science and Technology
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- OsloMet
- University of Agder
-
Field
-
PhD Research Fellow in Experimental Fluid Mechanics: Tunable hairy surfaces for droplet flow control
of the fellowship is research training leading to the successful completion of a PhD degree. The fellowship requires admission to the PhD programme at the Faculty of Mathematics and Natural Sciences. The application
-
/regulations.html#toc8 The purpose of the fellowship is research training leading to the successful completion of a PhD degree. The fellowship requires admission to the PhD programme at the Faculty of Mathematics and
-
learning experts across Integreat research themes. More about the position Machine learning is the mathematical and computational engine of Artificial Intelligence (AI). It is a fundamental force of
-
education in reservoir physics, applied mathematics, chemical engineering, geoscience, applied petroleum technology, or a related field, or must have submitted his/her master's thesis for assessment prior
-
one in Tromsø (UiT The Arctic University of Norway). Machine learning is the mathematical and computational engine of Artificial Intelligence (AI), and therefore it is a fundamental force of
-
personal qualities: Applicants must hold a master's degree or equivalent education in reservoir physics, applied mathematics, chemical engineering, geoscience, applied petroleum technology, or a related
-
-prediction benchmark studies. Depending on the qualifications and preferences of the candidate, the work may entail experimental investigations and/or modelling in the open-source computational fluid dynamics
-
computer science or statistics A solid background in mathematics, linear algebra and statistics. Documented experience with Bayesian spatiotemporal modelling, including experience with the INLA framework
-
in the open-source computational fluid dynamics (CFD) code PDRFOAM. The work will be conducted in collaboration with other research projects on hydrogen safety at the department. The position offers a
-
represented include: fluid mechanics, biomechanics, statistics and data science, computational mathematics, combinatorics, partial differential equations, stochastics and risk, algebra, geometry, topology