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research and education towards the integration of advanced medical image computing for supporting computer-aided disease diagnostics and intervention planning. Responsibilities and qualifications In
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at an international level developing deep learning, vision transformers, graph neural networks, foundation models, or related methodologies for integrating diverse imaging data with clinical, laboratory, and genomic
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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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, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted
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at the intersection of engineering and clinical medicine. You will lead the development of computer-assisted methods to support orthopedic applications, including diagnosis, surgical planning, and postoperative
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in machine learning and computer vision. Strong skills in developing computer vision and AI models. Experience with thermography or thermal imaging. Experience in data fusion and multimodal 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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plant phenotyping pipelines: Pipeline 1: UAV image processing and analysis for shoot trait extraction Development of a versatile software pipeline for processing and analyzing UAV-acquired imagery
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CO2 capture from the atmosphere. Your objectives will include to: Develop new optimization and/or machine-learning based reconstruction and segmentation algorithms to improve image quality in time