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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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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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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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on the microscopic level translates into the function on a macroscopic level. Imaging biomolecules, together with trace elements, is vital in understanding complex processes, disease mechanisms, or the effects
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techniques and data analysis to provide a more integrated picture of life processes in the context of health and disease. To be a postdoc fellow at the AMBER programme you will get unprecedented medical
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; significant practical experience in 3D image analysis or computer vision; knowledge and experience in scientific programming (python (preferred), Matlab or other relevant language) with application to image
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We are currently offering a PhD student position focused on developing new methods for molecular engineering that leverage live-cell imaging data and artificial intelligence (AI). The position is a
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include technique development work aimed at combining imaging techniques and data analysis to provide a more integrated picture of life processes in the context of health and disease. To be a postdoc fellow
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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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NEST partners, who collect and process biological behavior of live cells by imaging. The methods developed in this project will be used to improve the best practices in this application Who we