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model complexes. This research is part of a large, funded collaborative project supported by the Swiss National Science Foundation, involving partner researchers based in Germany, Switzerland, and France
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the design of innovative analytical and computational techniques to analyze,integrate and model multi modal data for the onset and progression of AMD. In doing so,the successful postdoctoral fellow will
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such approaches would be cost effective. To achieve this, you will be supported to undertake analyses using large data registries such as the Clinical Practice Research Datalink. This is an exceptional opportunity
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. The Schumacher laboratory in the Department of Biochemistry at the Duke University School of Medicine seeks a Postdoctoral fellow with expertise in Cryo-EM data collection, data processing, generation of cryo-EM
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inflammatory diseases including diabetes, cardiovascular, and Alzheimer’s disease. Successful candidates will lead projects using multi-omics datasets from large sample sizes of disease relevant tissues/cells
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Responsibilities for the role include: Data collection, cleaning, and merging from large-scale microdata sources (e.g., patents, dissertations, bibliometrics). Conduct data analysis using econometric and statistical
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astronomy. We develop and operate commercial-quality, cloud-based software application and platform services used by 10s of thousands of researchers to manage their large–and growing–data management
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Posting Details Position Information Classification Title Temporary Hourly Employment Category Temporary Is this an Hourly position? Yes Work Hours/Week 0-40 Overtime Eligible? Yes Position Location
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around 30,000 people in nearly 15,000 households. SOEP aims to capture social change and thus handles a constant stream of new and diverse topics and tasks. Its data collection and generation adhere
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Experience analyzing and interpreting large-omic datasets. Multi-disciplinary experience in integrating cellular and molecular mechanisms and data with phenotypic, physiological, and psychological data