Sort by
Refine Your Search
-
Listed
-
Program
-
Employer
- University of Oslo
- University of Bergen
- UiT The Arctic University of Norway
- NTNU Norwegian University of Science and Technology
- Inland Norway University of Applied Sciences
- NORWEGIAN UNIVERSITY OF SCIENCE & TECHNOLOGY - NTNU
- Norwegian Institute of Bioeconomy Research
- University of South-Eastern Norway
- Western Norway University of Applied Sciences
-
Field
-
of the forest sector. The position has a three-year term and the place of work in Ås, 30 km south of Oslo. Main responsibilities Developing AI approaches and methods for analyzing image, lidar data and x-ray data
-
within two months after the closing date for applications. You must have a keen interest in cellular and molecular neurophysiology and imaging and a strong motivation to work on challenging problems
-
months after the closing date for applications. You must have a keen interest in cellular and molecular neurophysiology and imaging and a strong motivation to work on challenging problems related
-
. The project entitles "Imaging Ageing Endothelium at the Nanoscale" and the position is funded by the EU Horizon Europe MSCA Program "Doctoral Networks" (DN) ImAge-D. The co-supervisor will be Professor
-
of maturity, this technology requires to experiment a change of paradigm to guarantee a sustainable development for the years to come. One pathway to achieve this paradigm shift relies on the development
-
and master's level in engineering and science, as well as PhD education in computer technology. The Mohns Center for Innovation and Regional Development researches innovation and offers master's
-
with the rise of single cell methods and advanced subcellular imaging technologies, have opened up new and exciting opportunities to study immunology. We are seeking a highly motivated and self-driven
-
-source parameters including imaging, proteomic and genomic data as well as life-style patient data from wearables to enable personalised vascular disease management. The VASCUL-AID platform consists of AI
-
trustworthy AI-driven platform (VASCUL-AID) that integrates multi-source parameters including imaging, proteomic and genomic data as well as life-style patient data from wearables to enable personalised
-
and functional analysis, and as a starting point build on the literature related to preconditioners for Newton methods. Concrete model problems and numerical examples will be used to guide and prototype