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for an early career researcher to contribute to the TRRC’s goal to strengthen causal inferences while developing a range of skills in educational measurement and statistics. Current projects involve propensity
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and clinical data. Qualifications Qualifications PhD in Computer Science, Electrical Engineering, Biomedical Engineering, Statistics, or a related field. Strong background in machine learning, data
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creative individuals. • Ph.D. in Mathematics, Computer Science, Statistics, Physics, Engineering, or a related field • Prior experience in modeling viral infections, bacterial infections, and/or immune
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diverse data sources to create reliable datasets, focusing on both large datasets and those with scarce data. Apply statistical analysis and machine learning techniques to extract valuable insights
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statistical analyses and data visualization, using R programming or a related language. Excellent written and oral communication skills. Ability to work independently or as part of a team. Position details
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processes and carbon-nitrogen interactions. The research will involve extensive field and lab experiments, statistical data analysis, review/meta analysis, report and scientific article preparation, and
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-health/e-health, data management, big data analysis, multilevel statistical analyses, machine learning, and a track record of publishing in peer-reviewed journals Qualifications Qualifications
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managing large data sets, qualitative risk assessment, and statistical predictive modelling of disease occurrence or spread and impacts of mitigation measures. Experience with either; animal handling
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other statistical techniques, Bayesian inference for phylogenetic and biogeographic hypothesis construction, and paleo community analysis with an emphasis on articulated brachiopods as focal taxa