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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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groups. The theoretical methods include stochastic modelling, MD simulation, and Bayesian inference; the position will also include joint collaborative projects with other group members and our
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theoretically, in tight collaboration with experimental groups. The theoretical methods include stochastic modelling, MD simulation, and Bayesian inference; the position will also include joint collaborative
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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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. 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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the surrogate forward models with a Bayesian inverse modeling framework to achieve real-time or near-real-time uncertainty quantification, such that we can efficiently resolve the uncertainties rising from rock
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, Uncertainty quantification, Approximation Theory, Applied Probability and Bayesian statistics, Optimal Control and Dynamic Programming. Appointment, salary, and benefits. The appointment period is two years
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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