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Field
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learning, and AI applications in radiology. The research area includes innovative work on developing Deep Learning Based Image reconstruction in CT on Photon Counting Detector CT with work in collaboration
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Engineering, Medical Image Analysis, Applied Mathematics or a related field Experience with deep learning for image analysis, preferably in medical imaging Experience with generative modelling, ideally
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, biomedical engineering, medical imaging, or related field Experience in deep learning with practical implementation Strong Python skills and relevant frameworks Experience with large clinical imaging datasets
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, a novel spatial discovery proteomics concept that integrates microscopic cell phenotyping with deep-learning based image analysis and global MS-based proteomics. This unique method was recently
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programming and instrument control using Matlab, Python, Labview etc Machine / deep learning expertise Strong analytical skills and ability to work in a multidisciplinary team Excellent communication and
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-processing using conventional and deep-learning based approaches. Coordinate MR imaging studies to promote applications of MRI in the application of radiotherapy. Assist in clinical radiation therapy physics
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of datasets through the JWST Advanced Deep Extragalactic Survey (JADES) collaboration. The postdoctoral researcher will contribute to ongoing JWST programs and collaborative projects, while being
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during surgery or endoscopic exploration. This postdoctoral position aims at developing innovative deep learning algorithms to help histology classification. Both classical histology based on hematoxylin
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modeling with deep learning for the analysis of hyperspectral imaging data. The researcher will be responsible for the design and development of numerical models, including neural network architectures
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algorithms for ultrasound simulation, imaging, and quantitative analysis Customize machine learning / deep learning methods for image reconstruction Conduct human studies of the algorithms and techniques in