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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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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
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. This allows us to shift from the standard imaging question of "what is the structure of this sample?" to "how does the structure differ from the known design?". The second question can be answered with far