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imaging inside human long bones (https://doi.org/10.1007/978-3-030-91979-5_10). For optimal imaging inside bone, one must consider the challenging 3D wave physics imposed by bone tissue: wave refraction
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, pipelines, imaging datasets). Application deadline: March 1, 2026 For any informal enquiries, please contact: alex.schier@unibas.ch Where to apply Website https://academicpositions.com/ad/university-of-basel
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. This project requires high-end skills in Geospatial Engineering & Technology including GeoAI, Geospatial Model Building, Remote Sensing Data Analyses, Advanced Image Processing, Data and Image Mining along with
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projector in image reconstruction. Dual-energy imaging will be explored for quantitative determination of electron density (ρₑ) and effective atomic number (Zeff), for the inference of WAC-relevant indicators
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to both contribute to new advances in a field with real world applications. Your research focus will be on challenges related to computer vision for geophysical image data. You will investigate and develop
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-detecting instrumentation (including SPECT and PET), accomplishes computer processing and image enhancement, participates in clinical and non-clinical research, and provides images, data analysis, and patient
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acquisition and processing. Image and data results are submitted for interpretation by Nuclear Medicine physicians. Exercises professional judgment in the performance of this service following the Code
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Radiology - MRI Full Time 87158BR Job Summary The Department of Radiology is a 24-hour, seven day a week operation providing imaging services for adult and pediatric inpatients and outpatients
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) Apply now to Levine Lab - Electrophysiologist (Assistant Professional Researcher) Job #JPF05913 Oral & Maxillofacial Surgery / SCHOOL OF DENTISTRY / UC San Francisco Application Window Open date: December
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 2 days ago
resolution mammography images. The model should be trained using a combination of weakly supervised learning, for images without local annotations, and fully supervised learning, for images with corresponding