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The Department of Statistics and Somerville College are recruiting an Associate Professor of Statistics. The successful candidate will be an outstanding individual who is, or has the potential
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Press Control+M to start dragging object Skip to Main Content ptnbsid=ps8UMrTywFKMWcXDVI6aoa0Ybgc%3d Back Search Jobs Actions Careers Sign In New User Home Search NavBar My Preferences Help Sign Out Click here for help Location Location Columbia (460) Rolla (108) Kansas City (107) St. Louis...
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Statistical Analyst (Department of Family Medicine and Community Health) Job Profile Title Statistician Junior Job Description Summary The Mixed Methods Research Lab, in the Department of Family Medicine and
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Max Planck Institute for Evolutionary Anthropology, Leipzig | Leipzig, Sachsen | Germany | about 1 hour ago
into the history, diversity and capabilities of the human species. In the Department of Primate Behavior and Evolution, we develop statistical methods for demography and combine these with long-term life history
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at any time based on the operational needs of the work unit and at the discretion of the position's supervisor or administration. Desirable proficiency in the following areas: statistical hypothesis
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interdisciplinary collaborative statistics unit in the Biomedical Informatics Research Division within the Department of Medicine at Stanford University. The mission of Stanford's Quantitative Sciences Unit is to
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Norwich, UK. Background: The High-Resolution Microbiomics group uniquely focusses on both theoretical and experimental methods for microbial community analysis. Combining statistical bioinformatics with
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. or Ph.D student in Experimental Medicine, Epidemiology or Biostatistics. If statistics or biostatistics are not the applicant's primary discipline, should have completed at least introductory statistics
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statistical mechanics. The goal will be the introduction of global indicators of systemic vulnerabilities and measures of collective instabilities. The activity of the candidate will be 1) the introduction
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biological insight into the underlying causes of kidney diseases. These approaches include the development of new statistical analysis methods, meta-analysis across cohorts, and the linking of clinical data