Funded Ph.D. Research Assistant Positions in Space Science, Astrodynamics, and Spacecraft GNC

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 focuses on the dynamics, modeling, estimation, prediction, and autonomous operation of space systems. We combine fundamental principles of physics and engineering with advanced computational methods, optimization, and machine learning to address challenging problems in space science and engineering.

Current research areas include:

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

·        Spacecraft Guidance, Navigation, and Control (GNC) and Space Robotics: Autonomous proximity operations, relative navigation, vision-based pose and motion estimation of unknown space objects, spacecraft and space-manipulator trajectory planning and control, and hardware-in-the-loop experimentation.

·        Thermospheric Density and Space Weather Modeling: Physics-informed modeling and machine learning for thermospheric density estimation and forecasting, space-weather effects on the near-Earth environment, satellite drag modeling, and applications to orbit prediction and conjunction assessment.

·        Physics-Based Modeling, Estimation, and Scientific Machine Learning: Integration of physical models, dynamical systems, estimation, uncertainty quantification, optimization, and machine learning for prediction and decision-making in complex space systems.

Students will have opportunities to pursue both fundamental research and advanced applications, working with mathematical and computational models, real-world space data, numerical simulations, robotic experiments, and hardware-in-the-loop testbeds.

What We Look For

We are particularly interested in applicants with strong mathematical and physical foundations, meaningful prior research experience, and a demonstrated interest in space science, dynamical systems, or spacecraft autonomy.

Applicants from aerospace engineering, mechanical engineering, electrical engineering, applied mathematics, physics, space science, robotics, control engineering, and closely related disciplines are encouraged to apply. Applicants from computer science or other disciplines are also welcome, provided they have substantial preparation in physical modeling, dynamical systems, or relevant scientific and engineering applications.

Competitive candidates should demonstrate:

·        Strong foundations in mathematics and physics, including relevant preparation in differential equations, linear algebra, mechanics, numerical methods, and mathematical modeling.

·        Substantial technical depth in at least one area directly relevant to our research, such as astrodynamics, dynamical systems, guidance and control, state estimation, robotics, spacecraft dynamics, atmospheric or space physics, or physics-based scientific computing.

·        Strong analytical, mathematical, and computational problem-solving skills, including the ability to develop and analyze mathematical models of physical systems.

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

·        Meaningful prior research experience demonstrating the ability to formulate scientific or engineering problems, develop technical approaches, analyze results, and make independent research contributions.

·        Strong motivation, intellectual curiosity, persistence, and the ability to learn new theoretical and computational methods.

Relevant preparation depends on the research direction:

·        For spacecraft GNC, astrodynamics, and space robotics: Strong preparation in dynamics, control theory, estimation, optimization, orbital mechanics, or related areas is particularly valuable. Experience with computer vision, robotics, or machine learning is advantageous when combined with these foundations.

·        For thermospheric density and space-weather research: Strong preparation in physics, atmospheric or space science, orbital dynamics, physical modeling, numerical analysis, or scientific computing is particularly valuable. Experience with statistical modeling, data assimilation, uncertainty quantification, or physics-informed machine learning is advantageous.

Applicants are not expected to have expertise in all these areas. However, they should have substantial depth in at least one research-relevant technical area and the mathematical and physical preparation needed to develop new methods for scientific or engineering problems.

Important: Our group uses AI and machine learning as tools to advance physics-based modeling, estimation, prediction, and autonomous space systems. These positions are not focused on general-purpose AI/ML research. Experience in AI/ML alone, without relevant preparation in physical sciences, dynamical systems, or engineering modeling, is generally not sufficient.

We particularly value students who combine strong fundamentals, research maturity, independent thinking, and a willingness to learn new theories, computational methods, and experimental techniques.

Admission to the group is selective. Applicants should demonstrate both strong research potential and a clear connection between their prior preparation and the group's current research directions.

How to Apply

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

Please include:

1.  Your CV.

2.  Academic transcripts.

3.  A brief research statement describing:

o   Your prior research experience and your specific technical contributions.

o   Your strongest mathematical, physical, and technical preparation.

o   Your experience with dynamical systems, physical modeling, estimation, control, space science, or other directly relevant areas.

o   The specific research directions in the X-Bai Research Group that interest you.

o   Why your background and research goals are a strong fit for the group.

Applicants should review the group's research activities before contacting us:

https://sites.google.com/view/x-bai-rutgers/home