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, based within the Department of Neuroscience at Erasmus MC, develops innovative ultrasound imaging technologies for the brain to advance neuroscience research and refine patient care. Our expertise lies in
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estimation of 3D coronary hemodynamics (velocity, pressure, and wall shear stress fields); design computational pipelines that integrate image-based anatomy, blood flow physics, and uncertainty quantification
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advancing medical imaging and improving cardiovascular care? Join us in developing a revolutionary multi-aperture ultrasound platform that enables comprehensive 3D imaging and biomechanical assessment
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requested to be available. Where to apply Website https://www.academictransfer.com/en/jobs/363267/phd-in-computational-imaging-fo… Requirements Additional Information Website for additional job details https
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PhD in Computational Imaging for High-Throughput Applications (1.0 fte) As a PhD student, you will be embedded in the research group Computational Imaging and Deep Learning (CIDL), part of
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characterising 3D primary spheroid models for MCL, DLBCL, and Richter syndrome using patient samples from in-house biobanks. Implementing and fine-tuning state-of-the-art AI image segmentation models for automated
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measurement techniques such as Particle Image Velocimetry, InfraRed Thermography and Background Oriented Schlieren for the study of critical aerodynamic phenomena such as flow separation, transition
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microswimmers/microrobots that can move and interact autonomously in 3D environments, mimicking the complex dynamics of microorganisms in fluids. Living systems such as bacteria or algae exhibit remarkable
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with simulated data and 3D imaging data of skin samples. The successful applicant will be an integral member of the EU PhotoMel community, which offers an open, diverse and inspiring environment
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with simulated data and 2D and 3D imaging data of plant tissues (the thale cress Arabidopsis and the aquatic fern Ceratopteris). At a later stage, the models can be extended to three dimensions using