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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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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
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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
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understanding through an interdisciplinary approach combining micro-fluidics, with genomics, atomic-scale mineral dissolution measurements, biogeochemical rate modelling, high resolution 3D-imaging, isotope
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“Hydrogen storage for maritime and industrial sector.” The position is organized under the Department of Process, Energy, and Environmental Technology and reports to the Head of the Department. The Ph.D