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at the intersection of NLP and human-centred or social applications. Experience with LLM fine-tuning, uncertainty quantification, or evaluation of language model outputs is particularly desirable. About You To be
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audiences. Experience in trustworthy AI, uncertainty quantification, interpretability, robustness, high-stakes decision-making, law, finance or regulation would be highly desirable. The role offers
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Center (TrAC) at Iowa State University seeks a Postdoctoral Research Associate to advance uncertainty quantification for large language models and agentic AI systems. The successful candidate will develop
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machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model development. Complete simulation verification, model validation, uncertainty quantification, and documentation
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, thermal transport, or related experimental probes, including the effects of instrumental resolution, data reduction, and observable reconstruction. Experience with uncertainty quantification, covariance
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. By building on recent developments and requirements for uncertainty quantification in volcanic ash forecasting you will develop computationally efficient techniques that maintain the speed required
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new applications of machine learning and artificial intelligence to experimental data analysis. Make advances in one or more of: multimodal data fusion/joint inference; uncertainty quantification with
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audiences. Experience in trustworthy AI, uncertainty quantification, interpretability, robustness, high-stakes decision-making, law, finance or regulation would be highly desirable. The role offers
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for predictive modeling, high-dimensional optimization, uncertainty quantification, forward and inverse design, including the development of Digital Twin components, under the supervision of Prof. Douglas H
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system surrogates, or uncertainty quantification. Model Predictive Control (MPC), data-driven control, or hybrid model-based / data-driven controller synthesis (e.g., RL-MPC) for complex dynamical systems