Postdoctoral Research Associate – Cyber-Resilient Autonomy & Digital Twins

Postdoctoral Research Associate – Cyber-Resilient Autonomy &
Digital Twins

Hampton University Faculty

Position Highlights

TAN’s Lab (https://sites.google.com/site/thanhtantp/3-research-interests) at Hampton Univer-sity is seeking a highly motivated
Postdoctoral Research Associate to contribute to advanced research in autonomous systems, digital-twin simulation, and machine-learning-based resilience technologies. The successful candidate will work under the direction of Dr. Tan Le on cutting-edge projects involving autonomy, perception, and intelligent systems.

TAN’s Lab focuses on efficient artificial intelligence, emphasizing computationally lean, hardware-aware models capable of real-time operation on edge devices, autonomous platforms, and emerging quantum–classical systems. The lab applies efficient AI to
NextG wireless networks, developing algorithms for RF sensing and signal classification, edge–cloud resource allocation, joint control–computation–communication, heterogeneous network coexistence, robotic networks, UAV tracking and trajectory prediction, O-RAN cybersecurity, and privacy-preserving, trustworthy net-work management. These research challenges require a diverse AI toolkit—including quantum-encoded learning, graph neural networks (GNNs), graph attention networks
(GATs), and deep reinforcement learning (DRL) —each selected to match the physics, structure, and data characteristics of the task. Lab-developed models support classification, detection, prediction, segmentation, tracking, and decision-making with an emphasis on efficiency, robustness, and deployability in contested RF environments.

TAN’s Lab—formally the R3-Lab—extends efficient AI foundations into broader intelligent-systems research, integrating network-science time-series analysis, cyber-physical system resilience, and
AI-enabled autonomous robotics. The lab investigates how sensing, computation, and com-munication interact in dynamic environments and designs efficient AI models that remain reliable under uncertainty, adversarial conditions, and resource constraints.
Across all domains, TAN’s Lab emphasizes application-specific efficient AI tailored to mission-critical autonomous and cyber-physical systems. Research activities span autonomous robotics, NextG networks, environ-mental digital twins, and resilient machine-learning pipelines, unified by a commitment to efficiency, trustworthiness, and real-world impact.

Duties and Responsibilities

• Develop and maintain digital-twin simulation environments for

autonomous navigation and perception.

• Build and evaluate ML/TinyML/QML-based detection models for

cyber-resilient autonomy.

• Generate and analyze corrupted datasets (sensor spoofing,

adversarial noise, environmental perturbations).

• Integrate ML/QML modules into autonomy pipelines using ROS2,

Gazebo/Ignition, Python, and C++.

• Conduct simulation-based testing and performance

validation.

• Prepare research documentation, technical reports, and

publications.

• Mentor undergraduate researchers in simulation and ML

workflows.

• Collaborate with internal and external research partners.

Minimum Qualifications

• Ph.D. in Electrical Engineering, Computer Engineering, Computer

Science, Robotics, or a related field.

• Strong background in machine learning, robotics, or autonomous

systems.

• Proficiency in Python, PyTorch, ROS2, or simulation tools.
• Demonstrated research productivity (publications, projects, or

code repositories).

• Strong communication and technical writing skills.

Preferred Qualifications

• Experience with digital twins or simulation-based

validation.

• Familiarity with quantum machine learning or TinyML.
• Background in sensor fusion, SLAM, or robotic perception.
• Experience mentoring undergraduate or graduate

researchers.

Work Environment

• Collaborative research environment within Hampton University’s

R3-Lab.

• Access to advanced autonomy and simulation platforms.
• Opportunities for publication, conference participation, and

interdisciplinary collaboration.

Application Instructions

Interested candidates should submit the following materials to Dr.
Tan Le at Tan.Le@hamptonu.edu :

• Cover letter
• Current resume
• Names and contact information for three professional references

(included within the resume) Applications will be reviewed on a rolling basis until the position is filled.