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for parameter estimation, degradation prediction, and analysis of electrochemical and structural characterization data, including X-ray CT image reconstruction, segmentation and quantitative microstructure
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aims to explore to which extent machine learning methods can help with these tasks, e.g. object reconstruction and signal/background discrimination. This will be a focus in the project. One exciting
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ambitious researcher with a strong background and experience in biomechanics, finite element modelling (FEM), medical image processing, additive manufacturing (AM). The project will be carried out in close
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ultrasound–optical imaging, contrast agent design, and computational reconstruction, with applications in deep-tissue imaging, tumor microenvironment characterization, and functional sensing. This position
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could develop the algorithms, models and software that: design and simulate self-assembling DNA nanostructures and molecular machines; turn artificial molecular networks into images — reconstructing where
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(Efstratios) Gavves, part of VISLab (Video & Image Sense Lab ) at the Informatics Institute (IvI), Faculty of Science, University of Amsterdam. CyPhai pursues a single north star: algorithms that understand
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(STED) and Stochastic Optical Reconstruction Microscopy (STORM) Cryo-electron tomography Imaging data science and AI-based analysis A strong record of contributions to peer-reviewed research publications
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involving advanced imaging and biomarker data. Perform MRI pulse sequence development and/or image reconstruction, as applicableAnalyze and interpret complex neuroimaging data. Collaborate with
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sensing technologies (camera, Electromagnetic tracking system, FBG sensing) with continuum robotic systems and development of associated sensing and estimation algorithms 3D environment reconstruction based
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fluorescence tomography at beamline 2-ID-E and 2-ID-D with focus on bioimaging of soil aggregates, as well as computational framework for modeling and reconstruction of related 3D datasets. The successful