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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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wish to apply advanced mathematics, statistics and machine learning techniques in biology and to work with modelling data, e.g. 2D images and 3D scans demonstrated theoretical contributions within
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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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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
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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 in basic