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Field
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imaging and image-analysis workflows to ensure robust and reproducible measurements; • Analyse time-resolved microscopy datasets to reconstruct host-cell responses and infection trajectories; • Use Python
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procedures. The doctoral candidate will combine non-invasive electrocardiographic imaging (ECGi) with late gadoliniumenhanced magnetic resonance imaging (LGE-MRI) to create personalised models of cardiac
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algorithms for computer vision, image processing, imaging, and/or physics-informed reconstruction. Scientific track record with publications at internationally renowned venues in machine learning. Solid
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Research Associate, Radiology and Imaging Services Posting Number req26573 Department Radiology & Imaging Sci Dept Department Website Link https://radiology.medicine.arizona.edu/ Location Tucson Campus
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machine learning and physics to recover nanoscale information from imperfect images? Modern computer chips are built with features only a few nanometers across, yet manufacturers need to measure these
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? Join our team to develop next-generation multi-aperture ultrasound technology combining distributed sensing, adaptive acquisition, image reconstruction, and AI-driven imaging for groundbreaking clinical
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, image processing, machine learning, and spatial optimization to reconstruct cable surface alignments and geographic coordinates. Validate inferred routes against reference or field observations and
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the Research Council of Finland (RCF) consortium project focusing on the development of low-field MRI hardware, sequences, image reconstruction and applications, in the Quantitative MRI research group
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numerical methods for 3D reconstruction and camera motion estimation from higher-order geometric primitives, with particular attention to conics, silhouettes and quadric surfaces. The goal is to overcome some
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scattering, computational imaging, and intelligent sensing. Develop novel theories, methodologies, and technologies for high-resolution imaging, information reconstruction, and target characterization in