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of image analysis and machine learning with a minimum of 90 higher education credits. Relevant courses include, for example, image processing, computer vision, machine learning, deep learning and neural
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research and methodological development to design and implement novel computational models and solutions. A solid theoretical background and hands-on experience in digital image processing and deep learning
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Sintorn, Professor in digital image processing, at the Department of Information Technology and conducted alongside researchers developing computational methods with a particular focus on deep learning and
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background in biology, programming or mathematics is meritorious. Knowledge in medical image processing, image registration, and large-scale analyses of genetic (including Mendelian randomization), protein, or
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), Opn4 and Opn5, as photoreceptors not directly involved in image-forming vision has opened up for non-visual opsin studies. Our recent publication, using GFP reporter mice, provides evidence of early
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research is based on large and high-dimensional datasets across multiple modalities, including molecular, clinical and histopathology imaging data. Our computational pathology research is based
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: Molecular cloning RNA sequencing and data analysis Immunofluorescence imaging and confocal microscopy Live-imaging Image analysis Cell culture The following education and skills are required: PhD in biology
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backgrounds: Molecular biology, protein engineering, biochemistry. Optical engineering, fluorescence microscopy, image analysis: Development of microscopes and data analysis pipelines used to acquire and
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, such as molecular data (e.g. omics), imaging, electronic health care records, longitudinal patient and population registries and biobanks. To be a doctoral student means to devote oneself to a research
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KTH Royal Institute of Technology, School of Engineering Sciences Job description We are looking for a motivated candidate interested in biophysics and live-cell imaging to join the Advanced Optical