Inside Higher Ed
Job Purpose
On real-time task rescheduling for Earth observation missions, the postdoctoral researcher will lead the design, implementation, and evaluation of multi-agent reinforcement learning (MARL) algorithms that enable satellites to cooperatively adapt to disruptions such as communication blackouts, satellite failures, and dynamic observation demands. This role supports the broader mission of building scalable, resilient space systems capable of operating with minimal human intervention in contested and resource-constrained environments.
Nature and Scope The postdoctoral researcher will contribute to a federally funded research initiative focused on building the next generation of intelligent, resilient Earth observation satellite systems. The position involves full lifecycle development of a decentralized autonomy framework, from theoretical design to high-fidelity simulation and performance analysis. The successful candidate will operate at the intersection of aerospace engineering, artificial intelligence, and distributed systems, contributing both as an independent researcher and as part of a collaborative academic team. This role requires a high degree of innovation, systems‑level thinking, and the ability to translate theoretical advancements in multi‑agent reinforcement learning into practical solutions for dynamic, resource‑constrained space environments. The postdoc will also have the opportunity to shape future project directions, mentor junior team members, and co‑author publications for top‑tier conferences and journals.
Principal Accountabilities
Design and implement multi-agent reinforcement learning (MARL) algorithms for dynamic task allocation and coordination within LEO satellite constellations
Simulate satellite behavior under partial failures, communication blackouts, and mission disruptions
Develop and evaluate candidate reward functions that balance competing mission objectives (e.g., task priority, power consumption, latency)
Integrate peer‑to‑peer communication protocols and ground‑station feedback loops into the autonomy framework
Analyze performance through Pareto optimization, sensitivity studies, and robustness evaluations
Collaborate with interdisciplinary teams and contribute to peer‑reviewed publications
Core Competencies
Reinforcement Learning & AI: Expertise in designing, implementing, and training single[1]agent and multi-agent reinforcement learning algorithms (e.g., Q‑learning, PPO, DDPG)
Space Systems Modeling: Proficiency in simulating orbital dynamics, satellite behavior, and space mission scenarios using tools such as Basilisk, STK, or GMAT
Autonomous Decision‑Making: Strong understanding of decentralized systems, autonomy architectures, and onboard task planning
Python Programming & Simulation Development: Advanced skills in Python, including development of modular, scalable simulation code
Data Analysis & Optimization: Experience with Pareto optimization, sensitivity analysis, and performance benchmarking of autonomous systems
Communication & Documentation: Ability to clearly present research findings through peer‑reviewed publications, technical reports, and conference presentations
Systems Thinking: Capable of integrating diverse subsystems (e.g., sensors, power,
Collaborative Research: Demonstrated ability to work effectively in multidisciplinary teams in undergraduate and graduate researchers.
Minimum Requirements Ph.D. in Aerospace Engineering, Space Sciences, Computer Science, Robotics, or a closely related field. Familiarity in reinforcement learning, multi‑agent systems, or autonomous decision[1]making. Experience with space mission modeling, orbital mechanics, or spacecraft subsystems. Proficiency in Python, with experience in simulation tools such as Basilisk, STK, GMAT, or equivalent. Familiarity with AI frameworks such as PyTorch, OpenAI Gymnasium, etc. Excellent written and verbal communication skills and a record of research publications
Compliance Salary Range Disclosure $75,000‑$85,000
Benefits
Health & Wellness: Comprehensive medical, dental, and vision insurance, plus mental health support
Work‑Life Balance: PTO, paid holidays, flexible work arrangements
Financial Wellness: Competitive salary, 403(b) with company match
Professional Development: Ongoing training, tuition reimbursement, and career advancement paths
Additional Perks: Wellness programs, commuter benefits, and a vibrant company culture
Join Howard University and thrive with us! https://hr.howard.edu/benefits-wellness
#J-18808-Ljbffr
Nature and Scope The postdoctoral researcher will contribute to a federally funded research initiative focused on building the next generation of intelligent, resilient Earth observation satellite systems. The position involves full lifecycle development of a decentralized autonomy framework, from theoretical design to high-fidelity simulation and performance analysis. The successful candidate will operate at the intersection of aerospace engineering, artificial intelligence, and distributed systems, contributing both as an independent researcher and as part of a collaborative academic team. This role requires a high degree of innovation, systems‑level thinking, and the ability to translate theoretical advancements in multi‑agent reinforcement learning into practical solutions for dynamic, resource‑constrained space environments. The postdoc will also have the opportunity to shape future project directions, mentor junior team members, and co‑author publications for top‑tier conferences and journals.
Principal Accountabilities
Design and implement multi-agent reinforcement learning (MARL) algorithms for dynamic task allocation and coordination within LEO satellite constellations
Simulate satellite behavior under partial failures, communication blackouts, and mission disruptions
Develop and evaluate candidate reward functions that balance competing mission objectives (e.g., task priority, power consumption, latency)
Integrate peer‑to‑peer communication protocols and ground‑station feedback loops into the autonomy framework
Analyze performance through Pareto optimization, sensitivity studies, and robustness evaluations
Collaborate with interdisciplinary teams and contribute to peer‑reviewed publications
Core Competencies
Reinforcement Learning & AI: Expertise in designing, implementing, and training single[1]agent and multi-agent reinforcement learning algorithms (e.g., Q‑learning, PPO, DDPG)
Space Systems Modeling: Proficiency in simulating orbital dynamics, satellite behavior, and space mission scenarios using tools such as Basilisk, STK, or GMAT
Autonomous Decision‑Making: Strong understanding of decentralized systems, autonomy architectures, and onboard task planning
Python Programming & Simulation Development: Advanced skills in Python, including development of modular, scalable simulation code
Data Analysis & Optimization: Experience with Pareto optimization, sensitivity analysis, and performance benchmarking of autonomous systems
Communication & Documentation: Ability to clearly present research findings through peer‑reviewed publications, technical reports, and conference presentations
Systems Thinking: Capable of integrating diverse subsystems (e.g., sensors, power,
Collaborative Research: Demonstrated ability to work effectively in multidisciplinary teams in undergraduate and graduate researchers.
Minimum Requirements Ph.D. in Aerospace Engineering, Space Sciences, Computer Science, Robotics, or a closely related field. Familiarity in reinforcement learning, multi‑agent systems, or autonomous decision[1]making. Experience with space mission modeling, orbital mechanics, or spacecraft subsystems. Proficiency in Python, with experience in simulation tools such as Basilisk, STK, GMAT, or equivalent. Familiarity with AI frameworks such as PyTorch, OpenAI Gymnasium, etc. Excellent written and verbal communication skills and a record of research publications
Compliance Salary Range Disclosure $75,000‑$85,000
Benefits
Health & Wellness: Comprehensive medical, dental, and vision insurance, plus mental health support
Work‑Life Balance: PTO, paid holidays, flexible work arrangements
Financial Wellness: Competitive salary, 403(b) with company match
Professional Development: Ongoing training, tuition reimbursement, and career advancement paths
Additional Perks: Wellness programs, commuter benefits, and a vibrant company culture
Join Howard University and thrive with us! https://hr.howard.edu/benefits-wellness
#J-18808-Ljbffr