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, synthetic data or data-driven decision support uncertainty quantification, robustness, variation simulation or tolerancing CAD/CAE integration, geometry assurance or quality data automation, control, 5G/6G
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inference pipelines for soft biomechanical systems, including differentiable physics engines, to support interpretable analysis, parameter estimation, sensitivity studies and uncertainty quantification. By
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. • Familiarity with vibration data analysis techniques. • Experience with Monte Carlo simulation, uncertainty quantification, or sensitivity analysis. • Programming skills in Python, MATLAB, R, or similar
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carbon, nitrogen, and water flows in agroecosystems. A solid background in uncertainty quantification, applied statistics, Bayesian calibration, and Monte Carlo simulations. Strong skills in scientific
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 11 hours ago
, geostatistics, and uncertainty quantification. Build written and oral communication skills through peer-reviewed publication, presentation at technical conferences, and collaboration with a broad array of
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with one or more of: Health claims data, EHRs, or other large-scale health/administrative datasets Environmental, climate, or air pollution exposure data Causal inference methods Uncertainty
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architectures (e.g. neural ODEs, flow matching and continuous normalising flows), robustness, interpretability and uncertainty quantification; collaborate with researchers across the ACT to identify emerging
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for experiments, or uncertainty quantification Experience with autonomous, self-driving, or robotic laboratory platforms Background in electronic polymers, conjugated polymers, organic semiconductors, soft
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Experience in one or more of the following areas: object detection and segmentation, multi-object tracking, time-series analysis, probabilistic modeling and uncertainty quantification, real-time or streaming
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simulations, density functional theory (DFT), molecular simulations, or machine-learning potentials. Experience with generative AI, active learning, uncertainty quantification, Bayesian optimization