58 post-doc-image-engineering-computer-vision Postdoctoral positions at Argonne in United States
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-disciplinary analysis efforts, and shape a growing research program. The successful candidate will receive strong mentorship and autonomy to develop a scientific vision, build collaborations, and pursue high
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We invite you to apply for a Postdoctoral Appointee position in the Chemical Sciences and Engineering Division (CSE) at Argonne National Laboratory. This position focuses on advancing research in
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We invite you to apply for a Postdoctoral Appointee position in the Chemical Sciences and Engineering Division (CSE) at Argonne National Laboratory. This position offers the opportunity
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field. Solid knowledge, and independent research capability in optimization, computing, power system engineering with track records of publications. Proficient in implementing control and optimization
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source for EIC and the design of the ePIC Barrel Imaging Calorimeter. Argonne National Laboratory, situated near Chicago, is a prominent multidisciplinary science and engineering research center. We are
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engineering and nuclear-chemical engineering systems Create new reduced-order models and submodels related to the fluid flow, heat transfer, thermochemistry, and electrochemistry in multiphase systems Use
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-be completed (typically within the last 0-5 years ) Ph.D. in engineering, operations research, computer science, applied mathematics, or a related field. Demonstrated expertise in mathematical
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The X-ray Imaging Group (IMG) at the Advanced Photon Source (APS) is seeking a postdoctoral researcher with expertise in biomedical imaging and image processing to develop advanced processing
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The Argonne Leadership Computing Facility’s (ALCF) mission is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing
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for such models include high-resolution 3D imaging, time-resolved materials characterization, and atomic structure determination. Scientific instrument data is often multimodal in nature and developing DL models