The Flight Dynamics and Control Laboratory at The George Washington University brings together differential geometry, control, estimation, optimization, and AI to advance autonomous systems. Our research spans mathematical theory, computational methods, and UAV flight experiments. We welcome prospective doctoral students who want to build depth in one of these areas while connecting their work to the others.
Explore our research, experiments, and team to learn more about the lab.
Ph.D. research opportunities
We have multiple fully funded Ph.D. positions in the following research directions. Contact Professor Taeyoung Lee to discuss research fit and potential starting terms.
Geometric Learning and Control
We develop learning and control methods that use the symmetries and geometric structure of autonomous systems. Topics include reinforcement learning for aerial robots and perception and decision-making methods that support human–AI collaboration. The goal is to connect learning with physical structure and reliable feedback control.
Relevant experience: Geometric mechanics or nonlinear control; deep reinforcement learning; computer vision; robotic simulation or flight experiments.
Explore the research: Equivariant reinforcement learning · Persistent and revisable robot maps
Stochastic Hybrid Systems and Estimation
We study how uncertainty evolves in systems that combine continuous motion with discrete events, such as impacts and changes in contact. Our work develops uncertainty propagation, Bayesian estimation, and stochastic control methods on manifolds, connecting mathematical analysis with practical computational algorithms.
Relevant experience: Probability and stochastic dynamics; hybrid systems; estimation or optimization; numerical methods and high-performance computing.
Explore the research: Uncertainty through impacts and changing dynamics · Fourier analysis on SO(3)
Perception and Autonomous Flight
We develop sensing, navigation, and control methods that allow UAVs to operate around moving platforms and in challenging maritime environments. Research combines visual and radar-based perception, inertial sensing, onboard computation, and experimental validation, including ship-relative navigation and autonomous landing.
Relevant experience: Visual-inertial navigation or sensor fusion; computer vision; embedded systems; C++, Python, or ROS; robotic hardware and flight testing.
Explore the research: Vision-based maritime flight · Ship-relative pose estimation with LiDAR
Preparation and research fit
A strong foundation in mathematics and experience writing structured, maintainable software are valuable across our projects. Applicants may bring strengths in engineering, applied mathematics, computer science, or related fields. The experience listed above describes useful preparation for each direction; applicants are not expected to have expertise in every topic.
In your inquiry, explain the research questions that interest you and how your coursework, projects, or prior research have prepared you to pursue them.
How to inquire and apply
Email Professor Taeyoung Lee at tylee@gwu.edu with the subject FDCL Ph.D. Inquiry — Your Name — Intended Start. Identify your preferred research direction and intended starting term, and attach a single PDF containing:
- Your CV, including publications if applicable.
- Your academic transcript; an unofficial copy is acceptable for the initial inquiry.
- A short summary of your relevant research or project experience and interests.
This email is an initial research inquiry. A formal application must be submitted separately through GW. Consult the Ph.D. program information and doctoral admissions requirements for current application instructions and deadlines.