Join Our Lab

We are always looking for highly motivated graduate and undergraduate students to join our lab who are passionate about

  • how things work and how to control them,
  • developing mathematical techniques to solve systems and control problems,
  • designing and building intelligent autonomous systems, and
  • bridging theory and application through creative numerical experimentation.

Research Directions for Graduate Students (examples)

  • Learning-augmented adaptive/autonomous flight control for unmanned aerial vehicles (UAVs) and multicopters
  • Real-time estimation and prediction algorithms for flows, atmospheric models, or robotic systems using machine learning + control theory
  • Robust/adaptive control for mechanical or aerospace systems in uncertain and dynamically changing environments
  • Integration of data-driven methods, optimization, and real-time control for mechanical and aerospace systems

Ideal Candidate Profile

  • Self-motivated and highly ambitious to learn
  • Bachelor’s or master’s degree in Mechanical Engineering, Aerospace Engineering, Robotics, Control Systems, Electrical Engineering, Applied Mathematics, or a closely related field
  • Strong foundations in control theory (classical and state-space methods) and dynamical systems
  • Programming proficiency in MATLAB/Simulink, Python, or other relevant simulation/control software
  • Interest and ability to engage in experimental hardware testbeds (e.g., UAVs, multicopter rigs, control platforms) as well as simulation development
  • Excellent communication skills, strong problem-solving ability, and the drive to publish in internationally peer-reviewed venues
  • Prior experience in robotics, autonomy, machine learning, estimation, or control is a plus but not strictly required

What We Offer

  • Full funding (stipend + tuition) for the duration of the PhD through research/teaching assistantships or fellowships
  • Access to state-of-the-art experimental infrastructure (including UAVs/multicopters, mechanical/aerospace testbeds, and simulation frameworks) and interdisciplinary collaborations
  • Mentorship in scholarly publishing and professional development, aligned with the lab’s active research portfolio (e.g., AIAA, IEEE, IFAC)
  • Opportunities to work on cutting-edge problems at the intersection of control theory, machine learning, and autonomy with real-world impact

How to Apply

Please submit the following materials in a single PDF to Dr. Ankit Goel (ankgoel[at]umbc.edu) with the subject line: “PhD Application – CELL”:

  • A cover letter describing your research interests, background, and motivation for this position
  • A CV including academic record, relevant coursework/projects, and programming/testbed experience
  • Contact information for at least two academic or professional references
  • (Optional, but highly recommended) A statement of research goals (1–2 pages) if you have a specific interest area in mind

If you satisfy the criteria listed above,

  • If there appears to be a good match, I will be happy to schedule a conversation to discuss your interests, assess your preparation, and describe current research opportunities in the lab.
  • If we both see a strong fit, you will be assigned a short project. The goal of this project is to evaluate your skills, research potential, communication and writing abilities, and capacity for independent and collaborative work. The duration of the project will depend on your level of preparation and the time you invest in it.

Prospective Graduate Students - Please Read Before Contacting a Faculty Member

Short version (TL;DR):
Do your homework before reaching out. Take time to understand the professor’s research areas and ask yourself whether there is genuine overlap with your own interests.

Long version:
Choosing a research advisor is one of the most important decisions in your academic career, so it is essential not to rush the process. Most professors support their students through competitive research grants—funding they secure by developing and refining their own research agenda over many years. As a result, faculty are generally unable to take on projects that are unrelated to their established research directions.

This does not mean you should ignore your own curiosity or simply take on whatever the advisor suggests. Rather, you should look for an advisor whose research genuinely aligns with your interests and strengths. Starting a collaboration where there is little overlap typically leads to frustration and wasted time for both the student and the advisor. More importantly, the cost of lost time is far greater for you than for the advisor.

Taking the time to identify a good match will set you up for a far more productive, enjoyable, and successful graduate experience.