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science, computer science, computer engineering, electrical engineering, and optical engineering, and frequently collaborates with partners in industry, academia, and other government organizations
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optimistic and conservative trajectories of the energy transition. Sensitivity analysis to quantify the influence of key uncertain parameters on total cost of ownership (TCO), GHG emissions, and strategic
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of methodologies to detect and locate seismic events recorded in DAS data Development and use of tools to estimate source parameters and fault mechanisms from DAS data Integrate and compare detection thresholds
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TPMS data, high-speed wheel encoders, CAN data, accelerometer data, and acoustics data. In addition to other cloud-based data for weather-friction estimates and crowdsourced vehicle data for estimating
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assessing the effects of Low Earth Orbiting satellites on LSST data and resulting systematic errors in dark matter and dark energy posterior cosmological parameter estimates. Skills must include database
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infrastructure monitoring, as well as connected autonomous vehicles Integrating multi-modal sensor data with physics-based models Developing robust and adaptive methods for real-time parameter and state estimation
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general predictive modeling methods, including model design, parameter estimation, sensitivity analysis, and model evaluation. An understanding of data acquisition and curation methods for real-world data
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general predictive modeling methods, including model design, parameter estimation, sensitivity analysis, and model evaluation. An understanding of data acquisition and curation methods for real-world data
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negotiable, preferably in autumn 2025 or in 2026. Background Northern wetlands emit large amounts of methane (CH4), a potent greenhouse gas. There are high uncertainties in the estimation of wetland CH4
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dynamics in the southwest Cordillera. ● Integrate geophysical and geochemical information (e.g., seismic, thermal, and compositional models) to constrain crustal rheology and structural parameters