Department News
Six RPI students have been awarded fellowships from the National Science Foundation’s Graduate Research Fellowship Program (GRFP).
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When we think of evolution, we think of a process that happens over hundreds or thousands of years. In research recently published, a team led by Rick Relyea, Ph.D., professor of biological sciences and David M. Darrin ’40 Senior Endowed Chair at Rensselaer Polytechnic Institute, found a species of frog that has evolved over the course of merely 25 years.
The adaptation was spurred on by something many assume is innocuous: salt.
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Rensselaer Polytechnic Institute’s Esther Wertz, Ph.D., associate professor in the Department of Physics, Applied Physics, and Astronomy, has been appointed Rensselaer’s faculty fellow of UPWARDS for the Future. UPWARDS stands for the U.S.-Japan University Partnership for Workforce Advancement and Research & Development in Semiconductors. It was established in May 2023 by Micron and Tokyo Electron Limited to “to bring together 11 universities from across the U.S. and Japan to develop leading semiconductor curricula, creating opportunities for cross-collaboration.”
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Recently, two teams from the Rensselaer Cybersecurity Collaboratory (RCC) competed in University of Connecticut's annual CyberSEED tournament.
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We all know how a bad night’s sleep can affect how we feel. In fact, the disruption of our sleep has been implicated at many levels of human disease, including cancer, diabetes, heart disease, and disorders associated with aging.
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When a star goes supernova, a massive burst of neutrinos is the first signal that can escape the density of the collapsing star. Detecting and analyzing this phenomenon in real time would allow us insight into stellar dynamics and, potentially, black hole formation. Detection of these types of signals from modern physics detectors is notoriously hard and presents computational challenges that push the bounds of modern and next-generation computing. Transmitting and analyzing the data from the massive particle physics detectors to the next generation of extreme-scale computing will require detailed modeling of the networking, hardware, and leadership class computing systems. These models will allow researchers to find and optimize the computing pathways, configurations, and infrastructure topologies so that they can handle these massive data loads.
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