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from multiple systems and sources to answer key operational questions. Deep experience using a variety of data mining/data analysis methods to build and implement dashboards, models and algorithms
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unify programs and curricula in data science with an initial emphasis on questions grounded in data that are generated by human activity, including computational social science (e.g., algorithmic
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, containerization (Docker), Kubernetes API development and web-based analytics tools Systems, Optimization, and AI ML/AI for mobility prediction and optimization Graph algorithms, network science Spatiotemporal
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predictive analytics Human factors, behavior science, and patient-centered design Advanced computing and scalable algorithms Decision science and learning health systems design Qualifications Required: Ph.D
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and simulation modeling • AI/ML, foundation models, causal inference, and predictive analytics • Human factors, behavior science, and patient-centered design • Advanced computing and scalable algorithms
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for mobility prediction and optimization • Graph algorithms, network science • Spatiotemporal modeling • Operational research for mobility and infrastructure • Real-World Practice • Prior collaboration with city
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data that are generated by human activity, including computational social science (e.g., algorithmic accountability and the interplay of data science with policy, law, and institutions), the economics
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and sources to answer key operational questions. Deep experience using a variety of data mining/data analysis methods to build and implement dashboards, models and algorithms that can help stakeholders
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algorithms. Excellent computer skills, including thorough familiarity with multiple programming languages, some experience in writing non-trivial programs, and familiarity with common algorithms, mathematical
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data that are generated by human activity, including computational social science (e.g., algorithmic accountability and the interplay of data science with policy, law, and institutions), the economics