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-time data acquisition and telemetry systems Familiarity with cloud computing platforms and edge deployment of ML models Experience with uncertainty quantification, sensitivity analysis, or robust
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navigation, trajectory planning, and point cloud mapping; and (3) deploy the developed algorithms on a physical mobile robot operating in GPS-denied, low-light, and geometrically repetitive environments
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. Experience working with satellite remote-sensing data such as Landsat, Sentinel, MODIS, SAR, LiDAR, or derived land-cover and vegetation product, and experience with in-the-cloud image processing Experience
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at scientific edge systems using large-scale HPC/AI computational and storage systems. Design and evaluation of ephemeral, user-configurable, and composable data and storage systems. Evaluation of cloud data
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computing runtime with application and compilers to efficiently integrate quantum processors with HPC resources. The research will explore different quantum computing modalities, their architectural
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structure, and simulation of coupled difference or differential equations. Experience calibrating simulation models against sparse, indirect, aggregated, or otherwise limited observations. Familiarity with
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., difference-in-differences). Contributions to reports, presentations, and peer-reviewed journal articles. Collaborate with ORNL scientists involved in these efforts, to gain expertise in the economic analysis
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-on experience in catalytic reactor design and performing experiments with data reduction are needed. Experience in handling different types of hydrogen isotopic samples and irradiated samples are also desirable
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at scientific edge systems using large-scale HPC/AI computational and storage systems. Design and evaluation of ephemeral, user-configurable, and composable data and storage systems. Evaluation of cloud data
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well as dynamic and transient inverter modeling and different applications of the simulation. Selection will be based on qualifications, relevant experience, skills, and education. You should be highly self