16 professor-computer-science-"https:" "https:" "https:" "https:" "University of Cambridge" Fellowship positions at Johns Hopkins University
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Technology (NIST) Professional Research Experience Program (PREP). NIST recognizes that its research staff may want to collaborate with researchers at academic institutions on specific projects of mutual
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capabilities to aid in the predictive engineering of biological systems, such as proteins, as part of the NIST Engineering Biology Program. Develop artificial intelligence and machine learning analysis pipelines
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of Standards and Technology (NIST) Professional Research Experience Program (PREP). NIST recognizes that its research staff may want to collaborate with researchers at academic institutions on specific projects
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Technology (NIST) Professional Research Experience Program (PREP). NIST recognizes that its research staff may wish to collaborate with researchers at academic institutions on specific projects of mutual
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Technology (NIST) Professional Research Experience Program (PREP). NIST recognizes that its research staff may want to collaborate with researchers at academic institutions on specific projects of mutual
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Technology (NIST) Professional Research Experience Program (PREP). NIST recognizes that its research staff may want to collaborate with researchers at academic institutions on specific projects of mutual
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applicants for a two-year postdoctoral fellowship in history of medicine and medical humanities. The fellow will have teaching and administrative responsibilities in the department's online program in
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motivation to publish and advance biomarker-based precision medicine. - Background in immunology, nephrology, rheumatology, pathology, molecular biology, computational biology, or related areas
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Dr. Meghan Moran and colleagues on a federally-funded research project. The fellow will receive training and mentoring on health communication, tobacco control, regulatory science, manuscript
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Bloomberg Distinguished Professor Domenico Giannone. Professor Giannone’s research lies at the intersection of economics, applied statistics, and data science, with a focus on advancing empirical and