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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 20 hours ago
Associate position involves laboratory research in biomedical science. The position studies focus on an animal model of arthritis and include in vivo studies (muscles and behavioral analyses, imaging
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that will be employed include flow cytometry, confocal and electron microscopy, in vivo two photon imaging, behavioral studies and transcriptomic techniques. The successful candidate will join the Center
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Postdoctoral Fellow position in the area of peptide chemistry or radiochemistry is available immediately in the Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine
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of the novel prototype Schwarzchild-Couder telescope. Scientific interests of the group are broad, and include the study of high energy emission from Galactic sources such as gamma-ray binary systems, supernova
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Post-Doctoral Position in Deep Learning for MRI Reconstruction at Yale University Title: Postdoctoral Associate, Yale School of Medicine Department/Division: Radiology and Biomedical Imaging
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on accelerating high-impact advances in human performance by leveraging the rapidly advancing fields of bioengineering, neuroscience, artificial intelligence, genetics, molecular biology, imaging, and regenerative
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Education, the Post Doctoral Associate will educate and teach medical students pedagogical strategies for the anatomical sciences and virtual microscopy of histologic images and cadaver dissections. Among
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analysis, animal models of airway and brain disorders, single-cell RNA-seq, multiplex cytokine assays, and immunofluorescence imaging. Assist in the establishment of performance standards, the selection
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will also be considered. Certifications/Licenses Required Knowledge, Skills, and Abilities Prior experience with cell culture, biochemistry, and cell imaging techniques. Must be able to work with rodent
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multimodal data (text, audio, video, images).Explore and implement new algorithms and methodologies to enhance the performance of LLMs in various real-world applications. 2. Model Development and