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fundamental physics Experience in one or more of the following ML research areas: Neuromorphic computing Uncertainty quantification Unsupervised/Semi-supervised learning and data mining techniques (clustering
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intelligence, statistical modeling, uncertainty quantification, optimization, or scientific machine learning methods. Demonstrated ability to evaluate alternative analytical approaches and determine when
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working with spatio-temporal datasets and remote sensing imagery Knowledge of distributed computing and uncertainty quantification Ability to function well in a fast-paced research environment, set
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validation, uncertainty quantification, robustness analysis, surrogate modeling, or adaptive feedback/optimization. Excellent written and oral communication skills. Motivated self-starter with the ability
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of analytics into production systems Knowledge of experimental design, uncertainty quantification, scientific machine learning, or digital twin methodologies Experience collaborating across national laboratories
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memory; multi-agent collaboration. Scientific Reasoning: Program/path-of-thought, tool-augmented and retrieval-augmented reasoning; uncertainty quantification and calibrated decisions. RL & Self-Improving
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for structured and unstructured data Familiarity with high-performance computing, cloud environments, or distributed data systems Familiarity with uncertainty quantification methods in AI/ML Ability to present
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memory; multi-agent collaboration. Scientific Reasoning: Program/path-of-thought, tool-augmented and retrieval-augmented reasoning; uncertainty quantification and calibrated decisions. RL & Self-Improving
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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