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Field
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for the position. Preferred selection criteria Experience with machine learning and neural networks Basic knowledge of MR physics Experience with signal processing and/or image processing Experience with Linux
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development work at the Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Experience with machine learning and neural networks Basic
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 2 months ago
analysis, image analysis, statistical, and machine learning methods, choosing the approach that best fits the scientific question. Investigate systematically what information is contained in imaging data
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develop a new generation of hybrid models combining large-scale machine learning with physical knowledge to represent interactions between mobile robots and their environment. The research will address
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Uppsala University, Department of Information Technology Are you interested in developing new image analysis and machine learning methods for precision medicine and clinical decision support? Would
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), Computer Science (Machine learning, Efficient Algorithms and High Performance Computing), and Physics (Image Formation Modelling). Your project is part of the DUAL-IMPACT project, which focuses on the development
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UiO/Anders Lien 4th October 2026 Languages English English English 3-years PhD position in probabilistic machine learning and statistics Apply for this job See advertisement About the position We
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machine-learning strategies for multimodal representation learning and for the integration of the complex biological datasets generated within the consortium. Duration and start date: 48 months, full-time
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(preferably in Python, including deep learning frameworks such as PyTorch); affinity with medical image analysis and computational modeling; experience with neural networks for image analysis, generative
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, segmentation, and metric-semantic mapping - Develop and evaluate machine learning-based image segmentation methods for robust perception in extreme environments; - Contribute to metric-semantic mapping