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to multitask and work independently as well as in a team. Experience in large-scale research project management and development, knowledge of statistics and big data analysis would be highly desirable. A highly
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. Analyzes large data sets. Analyzes next generation sequencing data (e.g., RNS-seq, whole genome/exome sequencing). Uses state-of-the-art bioinformatical and statistical tools. Develops new statistical
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include undertaking research, as appropriate, to support the project including contributing to the delivery and analysis of a large-scale questionnaire survey, conducting qualitative research interviews
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research environment supported by a team with broad expertise in data science, infectious diseases, hepatology, and clinical informatics. You will take a leading role in analysing large-scale, longitudinal
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further including to automated platforms to generate large statistical data sets. We will also experiment with untried higher spatial resolution techniques. The large, multi-dimensional data sets will be
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Experience in statistical or scientific programming (ideally R and/or Python) Experience in analyzing large and/or complex datasets Interest in quantifying uncertainties for computer models and/or climate
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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 large-scale infrastructure on fish connectivity and populations by advancing our understanding of critical ecological processes within these modified coastal seascapes. The project expects to generate new
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Nature Careers | Vancouver South Shaughnessy NW Oakridge NE Kerrisdale SE Arbutus Ridge, British Columbia | Canada | 3 days ago
samples such as serum/plasma, biopsies and whole blood for analysis and large databases, which include clinical information on patients. The group’s homepage: https://www.skane.se/en/about-us/research/for
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of Science at UvA. What are you going to do? The aim of the project is to use advanced Machine Learning techniques to predict the anharmonic vibrational spectra of large Polycyclic Aromatic Hydrocarbon (PAH