responsible for a single highly technical and complex piece of research equipment. 4. May review and evaluate the effectiveness of personnel. 5. May plan for and assign personnel to projects under control. 6. Operates with latitude for unreviewed action. 7. May receive general supervision conferring with higher levels only in unusual situations. 8. Performs other job-related duties as required. The Networked Autonomous Intelligent Learning (NAIL) Lab at the University of Houston is seeking a Postdoctoral Researcher to work on a newly funded NASA Early Career Faculty (ECF) project: “Safety-Enabled and Efficient Onboard Planning for Autonomous Spacecraft via Physics-Informed Reinforcement Learning.” (https://www.nasa.gov/directorates/stmd/space-tech-research-grants/ecf/early-career-faculty-ecf-2025-awards/)

The project aims to develop safe and computationally efficient learning-enabled onboard planning methods for autonomous spacecraft operating in uncertain deep-space environments. The research integrates physics-informed reinforcement learning, safety-critical control, uncertainty monitoring, and hardware-in-the-loop validation.
The postdoctoral researcher will play a leading role in several aspects of the project, including:
• Physics-informed and safe reinforcement learning
• Control barrier functions and safety-critical control
• Spacecraft dynamics, trajectory planning, guidance, navigation, and control
• High-fidelity simulation of autonomous systems
• Embedded implementation and hardware-in-the-loop validation
• Learning-enabled autonomy under model and environmental uncertainty

Candidates with a Ph.D. in robotics, control, aerospace engineering, electrical/computer engineering, mechanical engineering, computer science, or a closely related field are encouraged to apply. Strong backgrounds in one or more of reinforcement learning, nonlinear/optimal control, control barrier functions, autonomous systems, spacecraft GNC, or real-time/embedded implementation are particularly desirable.

MQ: Requires singular knowledge of a specialized advanced professional discipline or the highest level of general business knowledge, normally acquired through attainment of a directly job-related terminal degree or equivalent formal training in a recognized field of specialization that is directly related to the type of work being performed. No experience is required.

All positions at the University of Houston-System are security sensitive and will require a criminal history check.

The University of Houston System and its universities are Equal Opportunity Institutions. Everyone is encouraged to apply.

responsible for a single highly technical and complex piece of research equipment. 4. May review and evaluate the effectiveness of personnel. 5. May plan for and assign personnel to projects under control. 6. Operates with latitude for unreviewed action. 7. May receive general supervision conferring with higher levels only in unusual situations. 8. Performs other job-related duties as required. The Networked Autonomous Intelligent Learning (NAIL) Lab at the University of Houston is seeking a Postdoctoral Researcher to work on a newly funded NASA Early Career Faculty (ECF) project: “Safety-Enabled and Efficient Onboard Planning for Autonomous Spacecraft via Physics-Informed Reinforcement Learning.” (https://www.nasa.gov/directorates/stmd/space-tech-research-grants/ecf/early-career-faculty-ecf-2025-awards/)

The project aims to develop safe and computationally efficient learning-enabled onboard planning methods for autonomous spacecraft operating in uncertain deep-space environments. The research integrates physics-informed reinforcement learning, safety-critical control, uncertainty monitoring, and hardware-in-the-loop validation.
The postdoctoral researcher will play a leading role in several aspects of the project, including:
• Physics-informed and safe reinforcement learning
• Control barrier functions and safety-critical control
• Spacecraft dynamics, trajectory planning, guidance, navigation, and control
• High-fidelity simulation of autonomous systems
• Embedded implementation and hardware-in-the-loop validation
• Learning-enabled autonomy under model and environmental uncertainty

Candidates with a Ph.D. in robotics, control, aerospace engineering, electrical/computer engineering, mechanical engineering, computer science, or a closely related field are encouraged to apply. Strong backgrounds in one or more of reinforcement learning, nonlinear/optimal control, control barrier functions, autonomous systems, spacecraft GNC, or real-time/embedded implementation are particularly desirable.

MQ: Requires singular knowledge of a specialized advanced professional discipline or the highest level of general business knowledge, normally acquired through attainment of a directly job-related terminal degree or equivalent formal training in a recognized field of specialization that is directly related to the type of work being performed. No experience is required.

All positions at the University of Houston-System are security sensitive and will require a criminal history check.

The University of Houston System and its universities are Equal Opportunity Institutions. Everyone is encouraged to apply.