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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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. Candidates should have excellent programming, communication, and organizational skills. Ideally, candidates should have previous knowledge of image processing, statistics, and machine learning/AI, and
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Understanding (Prof. Dr. Martin Weigert) Research areas: Machine Learning, Computer Vision, Image Analysis Tasks: fundamental or applied research in at least one of the following areas: machine learning
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, machine learning, electrical engineering, or a related field. The ideal candidate will have some of the following skills such as: Computer vision and machine learning: A solid understanding of image and
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radioactivity. Important fields of application are radiotherapy to treat cancer, radiation diagnostics using X-rays, computed tomography, nuclear medicine and magnetic resonance imaging (MRI). We mainly work
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of scientific data, e.g. from image acquisition modalities or scientific simulations. Efficient algorithms are at the core of most of these data analysis and visualization applications. The focus of this Ph.D
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Your job Are you enthusiastic about understanding climate feedback processes in tundra ecosystems? Are you enthusiastic to conduct field- and remote sensing-based monitoring for impactful science
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. cell culture, molecular biology, imaging, cell signalling assays). As well as performing experiments on the project, the role will involve providing training in techniques to relevant staff/students
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Tomography (XCT), a non-destructive imaging technique, to perform crushing experiments of TRISO particles over a range of temperatures, thereby achieving a better understanding of the deformation behaviour
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for climate-positive and CO2-negative processes. What you will do Optimization of the pilot plant for the combustion of solid fuels (biomass, waste) with oxygen and subsequent CO2 capture Development of models