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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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existing studies, lake model simulations for emulator development and calibration. Use the emulator in a Bayesian statistical framework to quantitatively interpret paleoclimate proxy time series. Lead
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modeling and analysis. Ability to select, implement, diagnose, and adapt parameter-estimation or statistical-inference methods to suit the model, data structure, and scientific question. Experience with
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Computer Science, Robotics, Systems Engineering, Electrical and Computer Engineering, Mechanical Engineering, Chemical Engineering, Materials Science & Engineering, Chemistry, or a related quantitative scientific
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-time, and evidence-accumulation phenomena. Implement simulation, parameter-estimation, and model-comparison methods in Python, MATLAB, R, or related computational environments. Lead and co-author
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, active learning, Bayesian optimization, agentic AI, or closed-loop materials discovery. Experience in computational heterogeneous catalysis, electrocatalysis, surface science, electronic-structure analysis
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have the opportunity to contribute to various cutting-edge research areas. Several exciting research topics are proposed (not limited): Computer Vision / Remote Sensing for Agriculture: Explore
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of avian biodiversity information worldwide. Jointly, these approaches can provide complementary geographic and temporal coverage, and integrating them can improve estimates of species occurrence, phenology
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, ● Methods for heterogeneous treatment effects estimation, ● Methods for multiple exposures, multiple outcomes, ● ML and AI methods for causal inference, ● Bayesian causal inference, ● methods
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modeling, sensitivity and robustness analysis, Bayesian inference, inverse problems, parameter estimation, or model validation. Experience or strong interest in scientific AI/ML, including surrogate or multi