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to support a circular bioeconomy. We are seeking a highly motivated postdoctoral researcher to develop and apply machine‑learning and data‑science methods, with a focus on computer vision, to enhance wood
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Argonne’s Nanoscience and Technology Division seeks a postdoctoral scientist to advance transmission electron microscopy (TEM) studies of materials and interfaces relevant to microelectronics
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collaborative programme bringing together a team of leading experts in advanced electron microscopy imaging, first-principles modelling, metal halide semiconductor thin-film and device fabrication, and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 3 hours ago
for uterine fibroids using multimodal imaging and clinical information. The postdoctoral researcher will join a multidisciplinary research team consisting of computer scientists, neuroscientists, medical
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computed tomography (CT) reconstruction, including sparse-view and limited-angle algorithms, and the application of advanced machine learning (ML) and computational imaging methods to scientific and
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description As part of the ANR Fluid2Slip project, the postdoctoral researcher will have the
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optimization of optical imaging hardware, develop data acquisition software and algorithms for data processing, as well as perform phantom and human clinical studies. This candidate is expected to co-supervise
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will have connections to both the Molecular Imaging Program at Stanford (MIPS) and the Radiological Sciences Laboratory (RSL). The ideal candidate for this position will have interest in being trained in
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electrical engineering, biomedical engineering, computer science, medical physics, neuroscience, or a related field. While this is the preferred background, highly qualified candidates from other scientific
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engineering-based research applicable to cardiovascular disease as part of an extramurally funded project. The successful applicant will create computational models from medical imaging data and run simulations