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(NSSD). In this role, you will conduct fundamental research into the integration of Bayesian methodologies with system dynamics modeling, advancing statistical methods and the open-source scientific
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modeling, sensitivity and robustness analysis, Bayesian inference, inverse problems, parameter estimation, or model validation. Experience or strong interest in scientific AI/ML, including surrogate or multi
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track record of data and workflow tools and technologies development and machine orchestration methods (agentic and/or automated workflows), and HPC. Preferred Qualifications: Validated experience in
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machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model development. Complete simulation verification, model validation, uncertainty quantification, and documentation
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pipelines, model serving and inference at scale, and integration into scientific workflows (e.g., simulation, experimental facilities, and analysis platforms). You will guide technology selection and
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. Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other related definitions. Knowledge of federated learning SOTA algorithms. Knowledge of distributed
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infrastructure, including Docker-based microservices, large language model (LLM) inference servers on GPU clusters, vector database and retrieval-augmented generation (RAG) pipelines, and observability stacks
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. Major Duties and Responsibilities: Develop, maintain, and operationalize geospatial data science pipelines across ingestion, feature engineering, training, inference, evaluation, and delivery, using
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simulation tasks. Develop and maintain HPC-ready software workflows for distributed training, large-scale inference, scalable data ingestion, and data management on leadership-class computing systems and
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, multidisciplinary team environment. Preferred Qualifications: Knowledge of uncertainty quantification methods and causal inference for complex environmental systems. Experience with large-scale Earth system