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Field
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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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(this overlaps with the former themes as 2D and 3D vision is often integrated with language). Related papers by the Vision-CAIR members Theme A : Aligning Latent and Image Spaces to Connect
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such as quasi-normal modes. You will lead the development of modelling codes and have primary responsibility for at least one project within the broader research program. Additionally you will contribute
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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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microscopy, X-ray and electron imaging, scattering or spectroscopy to create in-house minerals properties databases that you will use to construct computational reaction models from resource to recharge, from
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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