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Catherine Schuman during Hour of Code

ORNL computer scientist Catherine Schuman returned to her alma mater, Harriman High School, to lead Hour of Code activities and talk to students about her job as a researcher.

The students analyzed diatom images like this one to compare wild and genetically modified strains of these organisms. Credit: Alison Pawlicki/Oak Ridge National Laboratory, US Department of Energy.

Students often participate in internships and receive formal training in their chosen career fields during college, but some pursue professional development opportunities even earlier.

Tyler Gerczak, a materials scientist at Oak Ridge National Laboratory, is focused on post-irradiation examination and separate effects testing of current fuels for light water reactors and advanced fuel types that could be used in future nuclear systems. Credit: Carlos Jones/Oak Ridge National Laboratory, U.S. Dept. of Energy

Ask Tyler Gerczak to find a negative in working at the Department of Energy’s Oak Ridge National Laboratory, and his only complaint is the summer weather. It is not as forgiving as the summers in Pulaski, Wisconsin, his hometown.

Edmon Begoli

Artificial intelligence (AI) techniques have the potential to support medical decision-making, from diagnosing diseases to prescribing treatments. But to prioritize patient safety, researchers and practitioners must first ensure such methods are accurate.

International Conference on Neuromorphic Systems (ICONS)

Materials scientists, electrical engineers, computer scientists, and other members of the neuromorphic computing community from industry, academia, and government agencies gathered in downtown Knoxville July 23–25 to talk about what comes next in

Molecular dynamics simulations of the Fs-peptide revealed the presence of at least eight distinct intermediate stages during the process of protein folding. The image depicts a fully folded helix (1), various transitional forms (2–8), and one misfolded state (9). By studying these protein folding pathways, scientists hope to identify underlying factors that affect human health.

Using artificial neural networks designed to emulate the inner workings of the human brain, deep-learning algorithms deftly peruse and analyze large quantities of data. Applying this technique to science problems can help unearth historically elusive solutions.