To aid in the search for elusive dark matter, Kevin Pedro looks for ways to harness the capabilities of artificial intelligence for analyzing particle collision data. For this work, Pedro was awarded the 2024 Universities Research Association Honorary Early Career Award.
Zorzetti’s research aims to help preserve quantum information by focusing on improvements to the way we transport it. The award provides $2.5 million over the next five years to support her work.
The U.S. Department of Energy awarded Guillermo Fernandez Moroni $2.5 million over 5 years to fund his project developing sensors to study the early universe. His work could enable surveying tens of thousands of distant galaxies at a time.
Guillermo Fernandez Moroni and Silvia Zorzetti are two of 93 early career scientists recognized by DOE for groundbreaking research.
Kavin Ammigan won an Early Career Award from the U.S. Department of Energy to research new materials for high-power particle accelerator targets.
The astrophysicist won the 2022 Early Career Award from the Universities Research Association for her work on multi-probe data analysis for the Dark Energy Survey.
The award is intended to support researchers early in their careers when many scientists do their most formative work.
Fermilab’s Pedro Machado has won the 2021 Universities Research Association Early Career Award for his theoretical work on neutrino science that helps experimentalists with novel search strategies and scientific questions worth exploring.
What if human analysis, combined with machine learning, could advance the study of the universe? The U.S. Department of Energy awarded Fermilab scientist Brian Nord a $2.5 million Early Career Research Award to explore that possibility. Nord has envisioned a new hybrid data-analysis method to undertake the project. It integrates the strengths of artificial intelligence and interpretations of statistics in ways that could potentially advance the studies of cosmology.
The U.S. Department of Energy awarded Farah Fahim an Early Career Research Award to investigate how deploying neural networks and machine learning on a particle detector can allow data processing at source. Her work could make data processing at detectors more efficient, improving fundamental research at physics facilities like the LHC at CERN.