Graduate Teaching Assistant positions are available in the AstroPlorers Lab (APL). APL aims to develop techniques and technologies for cost-effective and robust planetary exploration, transportation, and resource retrieval. Students are encouraged to apply analytical astrodynamics techniques—providing insights into dynamics governed by gravitational forces (Sun, Earth, Moon, etc.) and impulsive and continuous thrust—alongside numerical and empirical machine learning methods.
Graduate research projects include, but are not limited to:
- Design and optimization of spacecraft trajectories for cislunar transportation and beyond.
- Embedded navigation system design and development for autonomous proximity operations.
- Adaptive aerobraking for both trajectory design and atmosphere study.
- Space object tracking and orbit determination.
Resources:
- Access to the Texas Advanced Computing Center (TACC).
- UTA’s observatory for tracking space objects.
- Funding for developing and testing embedded on-board navigation systems.
- Support for attending conferences (e.g., AAS Space Flight Mechanics Meeting, Astrodynamics Specialist Conference, and AIAA SciTech)
- A graduate teaching assistantship that covers tuition, insurance, taxes, and offers an additional stipend of $1,700 to $1,800 per month for nine months a year. The teaching assistantship can be upgraded to a research assistantship.
- Outstanding U.S. candidates can compete for scholarships such as the Maverick Doctoral Bridge Fellowship.
This position offers a unique opportunity to contribute to the emerging cislunar economy, near-future planetary exploration, and space sustainability. Applicants should meet the minimum entry requirements for admission (see http://catalog.uta.edu/engineering/mechanical/graduate/#doctoraltext) and this position:
Essential skills:
- Bachelor’s degree in STEM
- Programming skills (MATLAB, C/C++, Python, and/or similar)
Desired skills:
- Master’s degree in STEM
- Knowledge of Astrodynamics/ Orbital/Celestial mechanics
- Knowledge of optimization techniques
- Experiences with hands-on experiments and hardware development
- Experiences with machine learning
- Independent thinking
To apply, please visit https://www.uta.edu/admissions/apply/ and note your interest in working with Dr. Hongru Chen at the MAE Department. Interested candidates are welcome to discuss their research plans with Dr. Chen.