Post Doctoral Fellow - Safe Learning and Spacecraft Autonomy - (496952 )

University of Houston; Houston (TX); On-site; USD 65,000 - 75,000; Full time

Job summary

The University of Houston, based in Sugar Land, TX, seeks a Postdoctoral Researcher for a NASA ECf project on onboard planning for autonomous spacecraft using physics-informed reinforcement learning. The role emphasizes safe, efficient learning-enabled control in uncertain deep-space environments.

The successful candidate will lead efforts in reinforcement learning, safety-critical control, spacecraft dynamics, and real-time hardware-in-the-loop validation, contributing to simulation and

Qualifications

• PhD or terminal degree in a directly related field of specialization related to the work.
• Strong background in reinforcement learning, nonlinear/optimal control, or autonomous systems is highly desirable.
• Experience with embedded hardware-in-the-loop validation is preferred.
• Security-sensitive position requires criminal history check.

Responsibilities

• Receives/reviews progress and evaluates results of experiments or projects under control or supervisory responsibility.
• Recommends changes in research, testing or experimental procedures.
• May be responsible for a single highly technical and complex piece of research equipment.
• May review and evaluate the effectiveness of personnel.
• May plan for and assign personnel to projects under control.
• Operates with latitude for unreviewed action.
• May receive general supervision conferring with higher levels only in unusual situations.
• Performs other job-related duties as required.

Skills

reinforcement learning

nonlinear control

optimal control

control barrier functions

autonomous systems

spacecraft GNC

real-time/embedded implementation

Education

PhD or terminal degree in a directly related field

Job description

Department: EngineeringTechnologySalary: Commensurate withExperience/Education

Description:

This position will be based in Sugar Land,TX

Possesses, understands, and applies a comprehensive knowledge in area of specialization. Develops understanding and skills to allow for completion of assignments that cross fields of specialization. Develops leadership and management skills.

1. Receives/Reviews progress and evaluates results of experimentsor projects under control or supervisory responsibility.
2. Recommends changes in research, testing or experimental procedures.
3. May be 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 levelsonly 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 environmentaluncertainty

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 advancedprofessional discipline or the highest level of general businessknowledge, normally acquired through attainment of a directlyjob-related terminal degree or equivalent formal training in arecognized field of specialization that is directly related to thetype 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.