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researcher to develop innovative quantitative weight-of-evidence approaches based on Bayesian networks for environmental burden of disease studies. The project aims to establish a novel framework for
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(NSSD). In this role, you will conduct fundamental research into the integration of Bayesian methodologies with system dynamics modeling, advancing statistical methods and the open-source scientific
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Engineering, Chemistry, Materials Science, or a closely related field Knowledge of heterogeneous catalysis, reaction kinetics, thermodynamics, and structure–reactivity relationships Ability to design
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Argonne National Laboratory, in the Applied Materials Division, has an immediate opening for a postdoctoral appointee. The candidate will develop physics-based models to predict the structure and
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Bayesian modelling framework to new products and dynamic storage conditions and integrating it into the MICROORC decision-support architecture; and (ii) development of data infrastructure and models
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materials by integrating literature, in-house, and newly generated experimental data Build surrogate and predictive models that connect composition, molecular structure, synthesis and processing conditions
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. We also take into consideration market benchmarks, if and when appropriate, and internal equity to ensure fair compensation relative to the university’s broader compensation structure. We are
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. We also take into consideration market benchmarks, if and when appropriate, and internal equity to ensure fair compensation relative to the university’s broader compensation structure. We are
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, machine-learning interatomic potentials, or related scientific machine-learning methods for atomistic systems. Familiarity with uncertainty quantification, active learning, Bayesian optimization, agentic
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, machine-learning interatomic potentials, or related scientific machine-learning methods for atomistic systems. Familiarity with uncertainty quantification, active learning, Bayesian optimization, agentic