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or equivalent. Skill in devising and performing experiments to acquire data, using and maintaining research equipment and instruments, compiling, evaluating and reporting test results. Knowledge and experience in
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Fall 2026 opening for a Postdoctoral Research Associate to work with the recently established Simons Collaboration on the Physics of Learning (physicsoflearning.org). The Simons Collaboration is
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strong foundations in machine learning and artificial intelligence, as well as a solid mathematical background. The position requires a strong interest in exploring multiple research directions toward
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
our group at the intersection of statistical methodology, machine learning, and biomedical data science. Our research develops rigorous and interpretable methods for high-dimensional biomedical data
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Scholar will work on individual projects as well as collaboratively with Professor Easwaran Narassimhan and Dean Kelly Sims Gallagher, CPL Co-Directors; Professor Travis Franck, Climate Modeling and Policy
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of physics‑informed control of mobile manipulators, data collection from real and simulated machines, and model development and testing in simulated environments. The project offers close collaboration with
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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Sumter enable students to earn associate or bachelor’s degrees through a combination of in-person, online or blended learning. All of our system institutions place strong emphasis on service — helping
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Sumter enable students to earn associate or bachelor’s degrees through a combination of in-person, online or blended learning. All of our system institutions place strong emphasis on service — helping
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states, charge density waves, superconductivity, and quantum magnetism - Kagome materials and superconducting hydrides - Machine learning interatomic potentials (MLIPs) and data-driven atomistic