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University of Texas Health Science Center San Antonio | San Antonio, Texas | United States | about 5 hours ago
Laboratory Sciences, Emergency Heath Sciences, Medical Sciences and Respiratory Care and master’s degree programs in Imaging Sciences, Medical Laboratory Sciences, Physician Assistant Studies, Respiratory Care
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related capabilities noted above). This leadership role offers opportunities to link to the broader imaging ecosystem at UQ, including HIRF (https://www.hirf.com.au/), and through external engagement
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-class experimental labs, digital design facilities, and a diverse community of interdisciplinary researchers. More information is available on the RCCS website. https://rccs.hw.ac.uk/research-theme/energy
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, administration of medication), observation of mice for recognition of normal and abnormal physical and behavioral changes, appropriate methods of anesthesia and euthanasia, genotyping skills such as tag/tail, DNA
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by integrating sparse sampling strategies, neural network–based reconstruction, and a virtual imaging platform. The goal is to develop fast, robust, and clinically viable quantitative MRI methods
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Excellence in Research ' Award from the European Commission. This is a recognition of the Institute's commitment to developing an HR Strategy for Researchers, designed to bring the practices and procedures in
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working with vision and image analysis Previous experience working with relevant human motion sensors Previous experience with experimental research and prototyping Previous experience in supporting
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, from 5 PM to 3 AM, and from 4 PM to 2 AM. The hospital supports PACS and voice recognition dictation. This position may offer flexibility for a hybrid remote work option for applicants with a residence
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candidates with a solid record of productivity and a neuroscience background or related fields are sought. Prior experience with mouse and/or primate models, confocal imaging, electrophysiology, molecular and
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classification and object detection, semantic segmentation, video analysis and action recognition, scene understanding, medical image analysis, self-supervised and unsupervised learning for vision, vision-based