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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
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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
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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
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containing magnetic resonance imaging (MRI) scans of individuals with MS who have participated in clinical studies. The goal is to identify MRI biomarkers for diagnosis, prognosis, and treatment response by
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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. Rigmor C. Baraas, the Molecular
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for clinical AI based on patient data from heterogeneous sources notably language/speech-based sources. The activity will focus on the development of a prototype implementation of early warning- and other AI
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reconstructions of glacier variability for selected areas in Norway. This involves landscape analyses using satellite images before field mapping. The time series will be based upon studies of sediments deposited
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learning-based image classification approaches. The objective is to quantify landscape changes over decadal timescales, with a particular emphasis on Western Norway. Relevant transformations include
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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
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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