"UCRO represents an incredibly unique opportunity for USRA to support the research efforts of an array of institutions, that will effectively advance the scientific and technical prowess of both USSF and AFRL. Importantly, these established partnerships will also engage a diverse group of students and postdoctoral fellows in research that contributes to an increased understanding and appreciation for STEM workforce opportunities within both agencies,” Senior Vice President Bernard Seery said in a public statement.
According to the announcement, the partnership aims to improve collaboration between USSF, AFRL and universities to advance meaningful scientific studies and provide research opportunities for undergraduate, graduate and postdoctoral fellows to boost interest in future science, technology, engineering and math (STEM) careers.
"USRA's role administering UCRO will enable institutions to generate pertinent research critical to both USSF and AFRL," USRA Director of Education Amanda Smith Hackler said in a public statement. "The research yielded will unquestionably support the mission of both agencies and will encourage interest in USSF and AFRL among the next generation of STEM professionals."
The principal investigators, institutions and proposals participating in the UCRO program include:
- Siamak Farhad, University of Akron: "On-Orbit and In-Situ Inspection and Repair of Spacecraft"
- Anouck Girard, University of Michigan: "Safe Learning in Space for Proximity and Rendezvous Operations"
- Brandon Jones, University of Texas at Austin: "Information-Theoretic Detection and Tracking for Rapid IOD"
- Michael Kinzel, University of Central Florida: "Industrial-Aero Optimization for Agile Rocket Cargo Delivery”
- Marek Osiński, University of New Mexico: "Designer Nanoparticles for Quantum Sensing of Location and Electric Field"
- Anthony Torres, Texas State University: "Understanding the Effect of Vibration on the Crystallization of ZBLAN"
- Miguel Velez-Reyes, University of Texas at El Paso: "Innovative Analysis of Spectra-Temporal Signatures Using Machine Learning for Ground-Based Remote Sensing of Unresolved Resident Space Objects"
