Researchers from JQI, UMD Department of Physics, NIST and QuICS are collaborating to understand quantum simulations of hyperbolic space.
New, most complete start-to-finish view of neutron star merger rewrites the way scientists understand these events.
Algorithms—like the ones that fill in words as people type—can learn to predict how and when proteins form different shapes.
NIH’s High-Risk, High-Reward Research Program will fund a project that could transform efforts to understand how neurons are wired.
Researchers in our college are using machine learning for applications that touch many aspects of our lives—from weather prediction and health care to transportation, finance and wildlife conservation.
In this episode of Relatively Certain, Dina Genkina sits down with JQI Adjunct Fellow Marianna Safronova, a physics professor at the University of Delaware, and JQI Fellow Charles Clark, an adjunct professor of physics at UMD and a fellow of the National Institute of Standards and Technology, to talk about how precision measurements with atoms might shed some light on matter that’s otherwise dark.