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for innovative microfluidic platform for exosome enrichment and MS analysis The Metabolomics and Analytics Center (MAC) is seeking a talented and motivated Postdoctoral Researcher to advance the integration
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science or another field related to the topic. You have experience in systematic data collection, statistical analysis with appropriate software, and, in particular, describing complex systems by simple
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In this role, you will be responsible of developing cutting-edge deep learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with
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in every aspect of breast cancer image interpretation in screening, including AI, image analysis, image display, and workflow. Tasks will consist of setting up and aiding in the execution of a
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learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with applications to medical imaging and robotic systems. In this role, you will
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handling platform and hanging droplet evaporator to process single cells, exosome samples and other biomass restricted samples for high resolution mass spectrometric analysis. As a Research Engineer, you
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of developing cutting-edge deep learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with applications to medical imaging and robotic systems. In
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work will be on reliability analysis and diagnostics of medium-voltage SiC MOSFETs, including the development of advanced test platforms for device and packaging evaluation. Key responsibilities include
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must have extensive expertise in deep learning for medical image analysis. We welcome applications that only partially qualify these requirements but make sure you argue why you are suitable
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coursework and GitHub repositories. Postdoctoral candidates must have extensive expertise in deep learning for medical image analysis. We welcome applications that only partially qualify these requirements but