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, including APIs, distributed services, databases, testing, and reusable software components. Experience with CI/CD, Git-based workflows, containerization, infrastructure automation, experiment tracking
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support high-performance computing, data privacy, and compliance requirements (e.g., HIPAA, IRB) Collaborate with data scientists, IT teams, and clinical stakeholders to translate ML/AI models into
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education; and (3) relevant preparation, including coursework and skills in mathematics, statistics, computing, and/or mathematics education, as well as any specialized training, research experience, or
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on department, college, and university committees and engage in service to the library and information science profession. Workload is generally distributed as approximately 60 percent teaching, 30
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. Workload is generally distributed as approximately 50 percent teaching and program leadership, 30 percent research, and 20 percent service, consistent with the responsibilities of a 12-month