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for a doctoral student. This position focuses risk-based inspection and maintenance planning for tunnels, including probabilistic risk updating, Value of Information analysis, and intervention program
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. Heterogeneous inspection and monitoring evidence is transformed into uncertainty-aware condition indicators, which are used to update risk, to assess whether additional information is worth collecting, and to
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the private sector. This project will directly impact various scientific fields, including chemistry, biology, and materials science, with applications in tech and medicine. (See https://www.nsf.gov/tip
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 5 days ago
and Bayesian methods. Experience using DOE computing allocations (NERSC, OLCF, ALCF). A record of productive collaboration in multi-institutional projects. Special Physical/Mental Requirements Special
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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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grow. We welcome you to join our community of faculty, students and alumni who are shaping the future of AI, Data Science and Computing. Dr Yingzhen Li (https://yingzhenli.net ) and her research group
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automation principles, self-driving labs, and/or robotic experimentation platforms. • Experience with sequential/active learning, clustering algorithms (e.g. KNN, random forests), Bayesian optimization, and/or
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, or sonar sampling of the benthos ● statistical fluency (e.g., mixed effects models, Bayesian statistics) applied to noisy field biomonitoring data or long-term ecological datasets ● experience operating and