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(ORNL) is seeking a highly motivated Postdoctoral Researcher with expertise in artificial intelligence and machine learning (AI/ML), remote sensing, Earth and environmental sciences, and the analysis
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, and remote-sensing data) to support model benchmarking, parameterization, and uncertainty quantification. Explore and apply AI/ML approaches (e.g., machine-learning emulators, surrogate modeling, AI
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guided by AI/ML, remote sensing, process-based modeling, field observations and experiments, and advanced analytical techniques. Major Duties/Responsibilities: Independently and collaboratively lead field
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independently, plan and arrange own work, and coordinate with management to prioritize projects. Preferred Qualifications: BS degree in data science, computer science, geospatial science, remote sensing
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), and uphold industry best practices in HPC/data center operations. Basic Qualifications: Advanced degree (MS or PhD) in Computer Science, Data Science, Geospatial Science (GIS/remote sensing), Electrical
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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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. The candidate will collaborate with multidisciplinary researchers in robotics, surveying, remote sensing, and artificial intelligence. This on-site position resides in the Building Envelope Materials Research
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analysis by integrating diverse datasets (e.g., in situ observations, remote sensing products, model simulations) to inform model development, calibration, and validation. Collaborate with a
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of nuclear nonproliferation, safeguards, nuclear fuel cycle analysis, remote sensing, or related national security mission areas. Record of peer-reviewed publications, conference presentations, or other
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Qualifications: Advanced degree (MS or PhD) in Computer Science, Data Science, Geospatial Science (GIS/remote sensing), Electrical/Computer Engineering, or a closely related discipline. Minimum of 10–12 years