Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
the group's research on developing novel machine learning/computer vision methodology. The focus of this project will be on the development of deep learning methodology for spatio-temporal medical image
-
practices. The findings will serve as the empirical foundation for the security framework. Defensive Strategies: Propose and prototype new defensive architectures and techniques that can be integrated
-
applying it for climate reconstructions through the late Quaternary and beyond. Our methodological focus has been on identifying and characterizing non-thermal factors or processes that potentially affect a
-
performed in close collaboration with experienced team members. Additionally, the candidate will acquire skills in performing in vivo PET/SPECT and MR/CT imaging experiments and data analysis. The candidate
-
in USN’s PhD-program in Ecology within three months of accession in the position. The vacant position is part of a collaboration between the Colour Vision and Retinal Imaging Laboratory headed by Prof
-
employees and 43,000 students work to create knowledge for a better world. You can find more information about working at NTNU and the application process here . About the position The Language Acquisition
-
questions related to the molecular regulation of autophagosome formation, using cell biological, genetic, and imaging-based approaches. The candidate will explore the function and regulation of proteins
-
-depth literature study of edge systems, distributed systems and simulation. You will perform experimental studies of computer systems with the emphasis on the time and energy consumption predictions. From
-
landscape analyses using satellite images before field mapping. The time series will be based upon studies of sediments deposited in glacier-fed distal lakes analysed with ultra-high-resolution scanning
-
national and international partners. The PhD project will focus on integrating advanced photogrammetric techniques applied to historical aerial imagery with modern deep learning-based image classification