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research and development agenda for computing facilities, advancing the transition from traditional HTC architectures to modern cloud-native systems, federated operational models, and AI-assisted monitoring
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environments aligned with University guidelines and relevant data-use regulations related to human-subjects research data. Supports cloud computing resources, including performance optimization, resource
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working with high-performance or cloud computing environments. Practical experience in either large-scale genomics data analysis or statistical/AI/ML method application to biological data. Familiarity with
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astronomy. We develop and operate commercial-quality, cloud-based software application and platform services used by 10s of thousands of researchers to manage their large–and growing–data management
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build consensus, successfully motivate others to move forward with a proposed architecture solution. Technical Expertise: Deep understanding of relevant technologies (cloud computing, databases
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knowledge of existing and emerging technologies, including public cloud offerings from Amazon Web Services, Microsoft Azure, and Google Cloud. Acts as a technical consultant and resource for faculty research
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diverse, high‑performing organization with a culture of accountability, inclusive leadership, and collaboration; sets clear goals, establishes career pathways, and embeds continuous learning cloud, AI/ML
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and educators in Statistical Models, Time Series, Big Data and Cloud Computing, Machine Learning and Artificial Intelligence, Human Computer Interaction, Quantum Computing, Swarm Intelligence, Advanced
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OR Statistical/AI/ML method development applied to biological data. Proficiency in programming languages and working with high-performance or cloud computing environments. Practical experience with sequencing
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required to make data driven discoveries that increase our scientific knowledge and improve the quality of life. We architect ecosystems of large-scale commons of research data, computing resources