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funded by the Wallenberg National Program for Data-Driven Life Science (DDLS) and the Wallenberg AI, Autonomous Systems and Software Program (WASP). The NEST project, Time-Resolved Imaging and Multi
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engineering, physics and mathematics. You will need strong written and verbal communication skills in English. Some level of familiarity with computer programming is rquired. Additional Merrits (documents
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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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center located in Norrköping, Sweden, with a PhD program in Analytical Sociology and an international Masters’ program in Computational Social Science. The research strengths of the IAS include the study
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array antenna systems for imaging MIMO radar in autonomous driving applications. This work will advance the design and characterization of intelligent devices and environments for wireless communications
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transfection work Experience with microscopy Experience with RNA-seq Experience with image and RNA-seq analysis, including competence using R or python and computational clusters Molecular biology techniques
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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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, computational modelling, bioinformatic analysis, and experimental vascular biology. Based in a dynamic translational research environment of data-driven life science, computational imaging, and vascular surgery
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. To meet the general entry requirements for doctoral studies, you must: Hold a Master’s degree in computer science, image analysis and machine learning, engineering, data sciences, applied mathematics
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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