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.