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Position as PhD Research Fellow in machine learning available at Department for Informatics with the research group Digital Signal Processing and Image Analysis (https://www.mn.uio.no/ifi/english/research
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Research Fellow in machine learning available at the Department for Informatics with the research group Digital Signal Processing and Image Analysis (https://www.mn.uio.no/ifi/english/research/groups/dsb
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for Informatics with the research group Digital Signal Processing and Image Analysis (https://www.mn.uio.no/ifi/english/research/groups/dsb/index.html) as part of Visual Intelligence (http://visual
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with the research group Digital Signal Processing and Image Analysis (https://www.mn.uio.no/ifi/english/research/groups/dsb/index.html ) as part of Visual Intelligence (http://visual-intelligence.no
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investigation will be based on image analysis, scanning electron microscopy (SEM), high-resolution microcomputed tomography (μCT), Hg-porosimetry, He-pycnometry, and gas adsorption of different lithologies with a
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regulating physiological processes, from growth and development to immune responses and adaptive behaviour. These peptides are typically translated as peptide-precursors that are proteolytically processed
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sampling for petrographic study. The seismic interpretation and evaluation will be used to test reservoir imaging and characterization approaches in complex volcanic-sedimentary systems. Petrophysical core
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description In all eukaryotic organisms, peptide ligands play a pivotal role in cellular communication acting as key players in regulating physiological processes, from growth and development to immune
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to test reservoir imaging and characterization approaches in complex volcanic-sedimentary systems. Petrophysical core-studies including measurements of velocity, density, porosity, and permeability will be
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. Perform quality control and imputation of genotype data, as well as pre-processing of neuroimaging data from relevant datasets, including international and Norwegian samples. Coordinate phenotype database