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for establishing a long-term vision for administrative applications, guiding modernization initiatives, and overseeing the successful transition from legacy systems to cloud-based platforms while maintaining
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of advanced statistical methodologies, and supporting research on high performance and cloud computing. The successful candidate will also be expected to offer 2-3 advanced technical or methodological workshops
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computing and/or cloud computing; familiarity with Earth system models through model development, model execution, and/or model performance diagnoses; applied mathematics methods such as machine learning
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. Parallel programming expertise. Experience developing research software outside of core domain knowledge. Experience working with cloud computing platforms (especially AWS). Experience with Docker and
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that allow for efficient self-service data retrieval and information processing across cloud and on-premises vendor platforms and data domains. Where data is shared across the division or university
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quantitative and computational social science, addressing a diverse array of new data and analytic challenges, facilitating impactful multidisciplinary collaboration, scholarly advancement, and the creation