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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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downstream imagery perception tasks including object detection and counting, visual question answering, semantic segmentation, and change detection. This position resides in the GeoAI Research Group in the
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anomaly detection, predictive analytics, and operational efficiency. Collaborate with infrastructure, network, application, security, and other technical teams to improve system health and
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engineering, or a related discipline 8-12 years of experience in gas detection and control systems Experience with experimental, custom laboratory equipment construction and implementation Supervision of
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of conduct and a statement from the Lab Director’s office can be found here: https://www.ornl.gov/content/research-integrity Deliver ORNL’s mission by aligning behaviors, priorities, and
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calibration approaches and apply machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model development. Complete simulation verification, model validation
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. Demonstrated research experience with AI and ML techniques. Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other related definitions. Knowledge of
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learning potentials (iv) modeling of molecular reaction, (v) Markov-state models for kinetics, and (vi) experience in coding and HPC workflows. Knowledge of or experience in Bayesian uncertainty
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observable reconstruction. Experience with uncertainty quantification, covariance modeling, sensitivity and robustness analysis, Bayesian inference, inverse problems, parameter estimation, or model
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data and storage systems. Evaluation of cloud data storage and analysis solutions (e.g., key-value stores, object or document storage, graph analytics systems) deployed on HPC computational and storage