Funded Ph.D. Research Assistant Positions in Space Systems, Autonomy, and AI

The X-Bai Research Group at Rutgers University is seeking highly qualified and strongly motivated Ph.D. students to join the group beginning in Spring, Summer, or Fall 2027. Fully funded research assistant positions are available for selected students.

Our research develops intelligent, autonomous, and reliable capabilities for future space systems. Current research areas include:

• Astrodynamics and Space Situational Awareness: orbit determination and prediction, uncertainty quantification, space-object characterization, and physics-informed modeling.

• Space Robotics and Proximity Operations: autonomous proximity operations, vision-based navigation, pose and motion estimation of unknown space objects, space-manipulator trajectory planning and control, and hardware-in-the-loop experimentation.

• Thermospheric Density and Space Weather Prediction: physics-informed machine learning for thermospheric density modeling and forecasting, satellite drag modeling, and their applications to orbit prediction and conjunction assessment.

• Physics-Informed and Uncertainty-Aware Machine Learning: integration of physics, data, and machine learning for prediction, estimation, planning, and decision-making in complex space systems.

Students will have opportunities to work on both fundamental research and advanced applications, using computational modeling, real-world space data, machine learning, robotic experiments, and hardware-in-the-loop testbeds.

What We Look For

We are particularly interested in applicants with strong technical preparation, meaningful prior research experience, and a clear research fit with the group.

Applicants from aerospace engineering, mechanical engineering, electrical engineering, applied mathematics, robotics, computer science, space science, automation/control, or closely related fields are encouraged to apply when their background and interests are closely aligned with the research areas above.

Competitive candidates should have:

• A strong foundation in mathematics, physics, and engineering fundamentals.

• Substantial preparation in one or more areas such as astrodynamics, dynamical systems and control, estimation, optimization, robotics, machine learning, or space science.

• Strong analytical, mathematical, and computational problem-solving ability.

• Solid programming experience, particularly with Python and/or MATLAB.

• Meaningful prior research experience demonstrating the ability to formulate problems, conduct technical analysis, interpret results, and contribute independently to a research project.

• Strong motivation to conduct rigorous, high-quality research and complete a Ph.D.

• Intellectual curiosity, independence, persistence, and the ability to learn unfamiliar concepts and methods quickly.

Knowledge of advanced control, estimation, optimization, statistics, machine learning, numerical methods, scientific computing, computer vision, or robotics is advantageous. Applicants are not expected to have expertise in all of these areas, but they should have substantial depth in at least one area directly relevant to the group's research.

We particularly value students who combine strong fundamentals with research maturity and who are willing to continually learn new theories, computational methods, experimental techniques, and modern research tools.

Admission to the group is selective. Applicants should be prepared to demonstrate both strong research capability and a clear connection between their prior preparation and the group's current research directions. Generic inquiries without a clear research fit are unlikely to receive consideration.

How to Apply

Interested students should contact Professor Xiaoli Bai directly at xiaoli.bai at rutgers.edu.

Please include:

• Your CV;

• Academic transcripts; and

• A brief statement describing:

  • your prior research experience;

  • your specific technical contributions to that work;

  • your strongest relevant technical background;

  • the research areas in the X-Bai Research Group that most interest you; and

  • why your background and research goals are a good fit for the group.