163 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" scholarships in Norway
Sort by
Refine Your Search
-
Listed
-
Program
-
Employer
- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
- University of Stavanger
- University of Oslo
- UiT The Arctic University of Norway
- Norwegian University of Life Sciences (NMBU)
- University of Bergen
- OsloMet – Oslo Metropolitan University
- OsloMet
- Western Norway University of Applied Sciences
- BI Norwegian Business School
- CICERO Center for International Climate Research
- Molde University College
- NHH Norwegian School of Economics
- NORWEGIAN UNIVERSITY OF SCIENCE & TECHNOLOGY - NTNU
- Nature Careers
- Østfold University College
- 7 more »
- « less
-
Field
-
/ ). By combining advanced machine learning techniques with qualitative methods, the project will investigate usage patterns and engagement levels with a health app across multiple European countries
-
(viability, proliferation, outgrowth, and invasion assays) is desirable. Experience with, or interest in, machine learning for the analysis of microscopy data and a strong ability to collaborate with
-
hazards, enhancing asset protection, maritime security, emergency preparedness, and societal resilience. The project will leverage advanced AI and machine learning techniques to enable predictive risk
-
broad range of areas, including causal inference and time-to-event analysis, clinical trials, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling
-
Digital. The research focuses on advanced signal analysis and machine learning methods that enable robust operation and service continuity in future wireless networks under challenging radio conditions. As
-
for machine learning models to optimise membrane properties, structure, and fabrication. The fellow will play a key role in the experimental part of the project, including: Preparation and characterisation
-
-state model will be approximated using machine-learning surrogates and will be used for a real-time optimization, such that the plant operates optimally despite disturbances. The candidate will be part of
-
the semantic foundation that enables AI systems to reason more coherently about ship designs, reducing ambiguity in the data available to machine‑learning systems, and supports explainability by grounding AI
-
), it is important that you are able to: Ability to work independently and in a team. Drive to learn new methods and applications. Curiosity and creativity in finding problem-related solutions. Contribute
-
may be considered if you can document that you are particularly suitable for a PhD education. You must meet the requirements for admission to the faculty's Doctoral Programme (https://www.ntnu.no