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eigenvalue problems, and large-scale graph analysis). Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting
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minimum of five years of relevant experience, beyond Ph.D. Demonstrated research experience in the applications of graph-theoretic analysis, probabilistic approaches to complex systems, time-series analysis
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machine learning, deep learning, foundation models, agentic AI systems, graph neural networks, and knowledge‑graph–based reasoning. Familiarity with integrating AI into scientific workflows at scale
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, or graph-based encodings in materials and molecular AI. Familiarity with frameworks for automated and reproducible workflows. Knowledge of governing regulations around privacy (e.g., HIPAA, ITAR), including
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, a strong emphasis in delighting customer and end-user needs. Preferred Qualifications: Active DOE Q clearance. Understanding of multidimensional and tabular modelling, vector databases, Graph DB
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storage and analysis solutions (e.g., key-value stores, object or document storage, graph analytics systems) deployed on HPC computational and storage systems. Co-authorship of peer-reviewed publications
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, a strong emphasis in delighting customer and end-user needs. Preferred Qualifications: Active DOE Q clearance. Understanding of multidimensional and tabular modelling, vector databases, Graph DB