Each year the National Science Foundation promotes the technical and leadership abilities of recognized young researchers by presenting them with NSF Early CAREER awards. A summary from the NSF website states, “The Faculty Early Career Development (CAREER) Program is a Foundation-wide activity that offers the National Science Foundation’s most prestigious awards in support of early-career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization. Activities pursued by early-career faculty should build a firm foundation for a lifetime of leadership in integrating education and research.”
In 2021, NSF recognized three promising young researchers from the UF Herbert Wertheim College of Engineering:
Charles Hages, Ph.D., Assistant Professor, Department of Chemical Engineering
Dr. Hages is concentrating his research on renewable energy. His NSF CAREER award is for “Developing solution-based thin-film chalcogenide perovskites.” These low-cost, non-toxic, stable photovoltaic materials can be produced from earth-abundant constituents and should be able to replace the current metal halide perovskites, exhibiting improved stability. The CAREER award will help fund his lab’s work for the next five years. (See the full story here.)
Yeongseon Jang, PhD., Assistant Professor, Department of Chemical Engineering
Dr. Jang’s research, “Engineering Self-Assembly of Recombinant Fusion Proteins for Bottom-Up Construction of Protein-Powered Synthetic Protocells,” will focus on designing synthetic versions of primitive cells powered by proteins that can be used for a wide range of applications such as particle delivery systems or even micro-bioreactors. Dr. Jang hopes to advance understanding of the basic rules of life through her research and to establish academic curricula for recombinant fusion proteins. (See the full story here.)
Victoria Miller, Ph.D., Assistant Professor, Department of Materials Science & Engineering
Dr. Miller’s research will utilize statistical methods and machine learning to investigate the micro-scale structure of materials during manufacture. Her project, titled “A Probabilistic Framework for the Nucleation of Recrystallization,” advances the ability to predict the initiation sites for microstructure changes can help prevent material failure during service in extreme environments, such as in aircraft engines or power plants. It can also be used to optimize materials processing during manufacturing, potentially reducing the cost of metals processing. (See the full story here.)