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-related issues. Demonstrated training or experience with using econometric methods (e.g., differences-in-difference, regression discontinuity, instrumental variables, fixed effects for panel data, matching
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following position Postdoctoral researcher (m/f/d) in Environmental Data Science and Machine Learning for the project BoTiKI Location: Görlitz Employment scope: full-time (40 weekly working hours) / part
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tools to differences in genotypes and environment to predict disease trajectories. The project is part of a Nordic initiative in large scale healthcare data analysis for development of precision medicine
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 21 hours ago
lifecycle (version control, documentation, and testing) Research experience with astronomical data analysis and large-scale astronomical survey datasets Special Physical/Mental Requirements Ability to sustain
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molecular biology, microbiology, biochemistry techniques in the area of Salmonella biology and pathogenesis. Managing large data sets, synthesizing and integrating different types of data to come to
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values rest on credibility, trust and security. By having the courage to think freely and innovate, our actions together, large and small, contribute to a better world. We look forward to receiving your
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background in quantitative research methods; Experience working with large-scale datasets; A keen interest in youth mental health, individual differences, and the role of education in shaping developmental
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values rest on credibility, trust and security. By having the courage to think freely and innovate, our actions together, large and small, contribute to a better world. We look forward to receiving your
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of computer simulation models that are open for reuse by health researchers and the NHS. The post holder will conduct cutting edge research that builds on our preliminary research with Open Weight Local Large
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processes for data standardization, data warehousing, quality assurance development and data analysis. A successful candidate will work closely in different aspects of the quality assurance and data