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Postdoc (f/m/d) Mechanical Characterization and Materials Assessment for Nuclear Applications / P...
approaches, provide a natural bridge between experiment and mechanistic understanding. Data-driven methods, for instance for inverse parameter identification or uncertainty quantification in fracture mechanics
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 2 months ago
. Data-driven methods, for instance for inverse parameter identification or uncertainty quantification in fracture mechanics datasets, are not an end in themselves, but a compelling extension where they
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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 14 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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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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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