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(45%) • Support statistical analyses of large data sets from an epidemiological cohort, investigating associations with birth outcomes using both targeted and nontargeted data (25%) • Support
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bioinformatics and spatial analysis techniques to large-scale spatial transcriptomics and imaging datasets, using tools such as MuSpAn to identify spatial biomarkers and uncover the biological mechanisms driving
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. Assists with the preparation and cleaning of worksite. Analyzes research data and summarizes results. Writes and may contribute to research report, papers, articles, and publications. Mentoring May assist
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at the Large Hadron Collider (LHC). The successful candidate will contribute to a broad research program that includes physics analysis, detector performance studies, experiment operations, and upgrade
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systems, or large-scale data pipelines. Evidence of open-source software development, reproducible research practices, and successful interdisciplinary collaboration. Demonstrated record of scholarly
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in the analysis and interpretation of single-cell multi-omic data. This role will contribute to cutting-edge research by integrating statistical and computational approaches to address complex
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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of real-world evidence using large real-world data sources, including data from the ongoing prospective cohort study (DREAM cohort). The successful candidate will have expertise in epidemiologic research
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working with human date and experience analysing large-scale omics datasets (e.g., proteomics, transcriptomics, genomics, metabolomics, or related high-dimensional biological data) are desirable
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evolutionarily conserved structured RNAs and candidate mammalian riboswitches from large genomics and transcriptomics datasets. The position will involve close collaboration with experimental scientists who will