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the cosmological information contained in the non-linear structure of matter fields and weak gravitational shear, beyond traditional statistics, by linking cosmological parameters to observables via physical models
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 12 days ago
the structure of matter, from subatomic particles to the large-scale structure of the universe. Our departmental instructional mission spans all segments of the student community. Over 60% of all college
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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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. 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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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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description The Sars-Covid virus genomic RNA encodes a small number of structural proteins, one of which is the N-protein. This N-protein plays a strong role in packaging the genomic RNA; yet, how this
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on the theory of deep learning will investigate questions such as the structure and expressivity of emerging neural architectures relevant to space, such as implicit neural fields, continuous normalising