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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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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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workflows into structured representations suitable for AI planning and optimization algorithms. 2) Research Leadership & Mentorship: * Abstract from the practical challenges presented by the work and
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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 committed
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. Documented research experience in modern deep learning (e.g. generative models, Bayesian deep learning or large pre-trained models) and excellent programming skills in Python and a modern deep learning
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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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a small fraction of the sample voxels need to be re-measured to detect the change, rather than the complete 3D volume. Microchip samples are also highly structured, with known design rules, which can
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a small fraction of the sample voxels need to be re-measured to detect the change, rather than the complete 3D volume. Microchip samples are also highly structured, with known design rules, which can
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finite-temperature anharmonic effects, thereby expanding the existing ab initio thermodynamic database to support the Bayesian inversion framework of the SHARP Thematic Project (Task 21, WP3). The project
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, Chemistry, Materials Science, or a closely related field Knowledge of heterogeneous catalysis, reaction kinetics, thermodynamics, and structure–reactivity relationships Ability to design and operate catalytic