The Department of Energy’s Office of Science has selected five Oak Ridge National Laboratory scientists for Early Career Research Program awards.The program, now in its twelfth year, provides support to exceptional researchers during their early care
Three scientists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory have been selected by DOE’s Office of Science to receive significant funding through its Early Career Research Program.
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IMAGE: From left: Marcel Baer, Nathan Tallent, and Ben Loer are recipients of a 2021 Early Career Research Program award from the U.S. Department of Energy. view more
Credit: Composite image by Shannon Colson | Pacific Northwest National Laboratory
A trio of Pacific Northwest National Laboratory (PNNL) researchers have each received a 2021 Early Career Research Program award from the U.S. Department of Energy (DOE) in the highly competitive program. Awards to DOE-based researchers total $500,000 per year for five years for salary and research expenses.
PNNL recipients are Marcel D. Baer, senior research scientist, and Nathan R. Tallent, computer scientist, both in the Physical and Computational Sciences Directorate; and Ben Loer, physicist in the National Security Directorate.
May 27, 2021 SHARE
Maria Piarulli, assistant professor of physics in Arts & Sciences at Washington University in St. Louis, is a recipient of the U.S. Department of Energy’s Early Career Research Program funding.
Piarulli
Piarulli was selected for her research program, “From Atomic Nuclei to Infinite Nucleonic Matter within Chiral Dynamics,” which falls under the Office of Science’s Nuclear Physics program office. The award is meant to support individual research programs of outstanding scientists early in their careers.
“My research aims to develop a clear and coherent picture in which microscopic models
accurately describe atomic nuclei while simultaneously predicting properties of infinite matter,” Piarulli said. “It will make use of state-of-the-art computational techniques and high-performance computing to broaden the applicability of the Quantum Monte Carlo methods.”