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testing. A campaign layer, driven by Bayesian optimization, decides which experiment to run next. The postdoc will own the system architecture below that layer: the PLC and instrument control, the software
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. Experience with uncertainty quantification, Bayesian inference, inverse modelling, parameter estimation, or model calibration. Experience with high-performance computing, surrogate modelling, reduced-order
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Missouri University of Science and Technology | Rolla, Missouri | United States | about 2 months ago
to have experience in several of the following areas: data processing, statistical analyses, R software, regression models, process-based models such as DSSAT or APSIM, Bayesian statistical analysis
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project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict