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Field
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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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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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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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international collaborations with clinicians, regulators, policymakers, and industry partners. You must have a strong background in machine learning, computer vision, and medical image analysis, with publications
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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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of reconfigurable nonlinear processing units (RNPUs, [Nature 577, 341-345, 2020[(https://www.nature.com/articles/s41586-019-1901-0). In this PhD project, you will work on the development of efficient machine learning
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projects Designing for complex or high-consequence systems Human-centred product, service, or interaction design in healthcare, defence, or critical infrastructure Applied human-computer interaction or
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of transmission electron microscopes and/or scanning electron microscopes Knowledge of artificial intelligence and machine learning and their applications in electron microscopy Knowledge of computer programming
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Biomedical Engineering conducts leading research in image analysis, computer vision, and machine learning, with a growing emphasis on generative AI and AI for scientific discovery. Our mission is to develop
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involve leveraging advanced natural language processing and medical image analysis to transform imaging data into clinically relevant information. Additionally, it will explore the use of multimodal fusion