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Field
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may contain unique information about organ quality that is not visible in blood or clinical characteristics. By combining the results of AI-driven image analysis of histological samples conducted in
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geometry altogether and operate in hyperbolic space. Our lab has published multiple papers showing that hyperbolic deep learning has strong potential for computer vision, from hyperbolic image segmentation
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. PhD Candidate Neuroimaging in Epilepsy Our goal: to leverage advanced Magnetic Resonance Imaging (MRI) techniques (including functional MRI to assess brain network metrics and MR spectroscopy to measure
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imaging, X-ray tomography/radiography).
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processes. In this project, we will leverage the advanced voltage imaging in larval zebrafish to investigate how intrinsic plasticity reshapes neuronal behavior. Your job Traditional electrophysiological
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The Department of Medical Imaging is one of the most active research departments of the Radboudumc. More than 100 researchers are continuously striving to optimize healthcare. Close cooperation
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quality that is not visible in blood or clinical characteristics. By combining the results of AI-driven image analysis of histological samples conducted in this PhD project with biomarker data and outcome
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representations. In this project, you will substantially improve quantitative magnetic resonance imaging (MRI) image quality using deep learning approaches. Quantitative MRI allows healthcare providers
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zebrafish—being small, transparent and responsive to threatening stimuli —enable whole-brain imaging of neuronal activity. This makes them an ideal model to study the neurons involved in these innate
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metabolism and performance. Working across in vivo, ex vivo, and in vitro systems, you will integrate advanced cardiac imaging, metabolic flux analysis, and biochemical profiling to connect molecular damage