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relevance and scale-up potential. About You We are looking for someone with significant experience in: Conducting independent and collaborative research within large, multidisciplinary and international
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advanced biostatistics Computer proficiency, including use of statistical software for spatial analyses, such as ArcGIS, QGIS, R, Google Earth Engine Experience assembling, organizing, and analysing large
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internal institution systems may be submitted. Click here for detailed information about acceptable transcripts. A current resume/CV, including academic history, employment history, relevant experiences
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Biochemistry Department is a large research and teaching department housed in a well-equipped building. We teach biochemistry, genetics, plant biotechnology, and related subjects to undergraduate and
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the use of large administrative data to understand and address inequality. OI has published seminal work on national trends in upward mobility ; racial disparities; the role of neighborhoods, social capital
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working with administrative data to inform operational decision-making and strategic planning within the law school. The ideal candidate will have strong quantitative skills, experience working with large
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social and ecological data on biodiversity and other benefits of perennial agricultural features. We seek candidates with expertise in landscape ecology, agroecology, agrobiodiversity, ecosystem services
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, developing and applying AI technology to large medical images and genetic data for understanding brain development and aging, and precision psychiatry. In the past years, the laboratory has conducted various
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Center for Astrophysics | Harvard & Smithsonian | Cambridge, Massachusetts | United States | about 5 hours ago
-linear optics, atoms and molecules in strong fields, atomic and molecular astrophysics, quantum information and quantum simulations, molecular astrophysics and laboratory cosmology. Applicants should state
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collaborate nationally through BioFAIR project and ELIXIR-UK Southampton Node. About you: You will have a quantitative or RSE background and an interest in applying computational and data-driven methods