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This project aims to enhance best practices in strain quantification for biomedical applications, facilitating the transition of image-based measurement methods from laboratory research to clinical
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Centrum Wiskunde & Informatica (CWI) has a vacancy in the Computational Imaging research group for a talented PhD student (m/f/x), on the subject of: Fast and low-dose medical imaging using multi
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to neuromonitoring and imaging. The ideal candidate should have an MD (or equivalent) or a PhD related to human physiology. Postdoctoral research fellows are trainees working in an apprenticeship mode in preparation
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to neuromonitoring and imaging. The ideal candidate should have an MD (or equivalent) or a PhD related to human physiology. Postdoctoral research fellows are trainees working in an apprenticeship mode in preparation
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annotations are scarce or unreliable. Recently developed unsupervised learning methods allow to circumvent this limitation by learning patterns in unlabelled medical images and then leveraging them
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journals. Close collaboration with team members and colleagues. Essential qualifications: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong
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: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong knowledge in Machine/Deep Learning with experience in discriminative models
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Roentgen’s Nobel Prize-winning discovery of X-rays enabled us to non-destructively image inside the body, birthing medical diagnostic imaging and revolutionising materials characterisation
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to circumvent this limitation by learning patterns in unlabelled medical images and then leveraging them for downstream tasks. In this project, you will develop novel unsupervised machine learning methods
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during pathological conditions. 2. Neonatal seizures and epilepsy 3. GABAA receptor physiology. We approach these research areas with electrophysiological and imaging. The Postdoctoral researcher will be