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Field
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simultaneous stimulation of these processes increases aggregate clearance. This project will employ a combination of advanced cell biology, molecular biology, live-cell imaging, and proteomics approaches. Your
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Biomedical Sciences-track Physiology of Synapses and Networks; supervising bachelor and master theses and tutoring students; co-supervising PhD theses; actively contributing to the development and improvement
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-supervising PhD theses; actively contributing to the development and improvement of the broader teaching programmes in the department; taking part in committees and working groups and carrying out departmental
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aims to study how the brain processes pictures and sequences of pictures, like in comics. These PhD positions will study how sequences of images combine images together into hierarchic relationships
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Biomedical Sciences-track Physiology of Synapses and Networks; supervising bachelor and master theses and tutoring students; co-supervising PhD theses; actively contributing to the development and improvement
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given to candidates with expertise in two or more of the following microelectronics domains relating to the position: Analogue and digital signal processing for communication, radar, GNSS, image and video
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combined with the ability to work efficiently both independently and as part of a team. Moreover, you have: a PhD in (bio)medical sciences/molecular imaging; research experience in molecular imaging
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of following the “Building with Nature” paradigm, to understanding how achieve a healthy biodiverse environment. We do frontier applied science, meaning that we link fundamental understanding to societal usage
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Radboud University Medical Center (Radboudumc); 10 Oct ’25 published | Netherlands | about 2 months ago
Image Analysis Group (DIAG) at Radboudumc. We develop, validate and deploy novel medical image analysis methods, usually based on the newest advances in machine learning with a focus on computer-aided
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reference architecture for data visiting. This paradigm enables algorithms to securely access and process data within the environments where it resides, supporting federated learning for training machine