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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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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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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
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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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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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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | about 1 month ago
, Applied Mathematics, Physics, Biophysics or a similar qualification Interest in and/or experience with AI/machine learning A willingness to learn and extend this knowledge and master new challenges in
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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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(or equivalent) in an appropriate discipline. Ideal candidate will have some prior knowledge in deep learning and computer graphics. Subject area: Medical imaging, biomedical engineering, computer science & IT
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Generative machine learning models have made significant progress in recent years. Typical examples include, for example, high-quality image or video generation using diffusion models (e.g
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formats available in conventional hardware are often too accurate for the needs of machine learning: they do not improve the quality of the trained model but may deteriorate it by causing overfitting