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about working at NTNU and the application process here. ... (Video unable to load from YouTube. Accept cookie and refresh page to watch video, or click here to open video) About the position To be able
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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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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
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utilizing human cell cultures (2D and organoids), advanced fluorescent imaging, live imaging, FACS, RNAseq + bioinformatic analysis, Click-IT technology, RT-qPCR, Western Blot, and possibly animal experiments
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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
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live-cell imaging of mitochondria in plants, algae, and marine metazoa with computational analysis to find the universal principles of mitochondrial motion across these species. The project is part of
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measurements, biogeochemical rate modelling, high resolution 3D-imaging, isotope labelling and integrated geobiological data analysis. Analytical approaches implemented can include a multitude of advanced