Postdoctoral Associate

The Data Security and Privacy Lab at Virginia Tech invites applications for a Postdoctoral Associate position in the areas of AI for cybersecurity, and cybersecurity and privacy for AI. The position is intended for a highly motivated researcher interested in advancing the foundations and applications of secure, privacy-aware, and reliable AI systems.

The postdoctoral researcher will work closely with the lab director Dr. Kantarcioglu, students, and research collaborators on projects at the intersection of machine learning, AI, security, and privacy. The position offers the opportunity to contribute to both theoretical and applied research, publish in leading venues, and help shape new research directions in these areas.

The successful candidate is expected to contribute to one or more of the following areas:

• AI for cybersecurity, including the use of machine learning and large language models for cyber defense.
• Privacy for AI, including privacy-preserving machine learning, privacy risks in model training and deployment, data protection, and auditing or evaluation of privacy mechanisms.
• Graph neural networks, including robust learning on graph-structured data, security and privacy issues in GNNs, graph-based anomaly detection, and trustworthy graph machine learning.

Required Qualifications

• PhD in Computer Science, Computer Engineering, Electrical Engineering, or a closely related field.
• PhD must be awarded no more than four years prior to the effective date of appointment with a minimum of one year eligibility remaining.
• Strong research background in machine learning, cybersecurity, privacy, graph machine learning, or a related area.
• Demonstrated publication record in relevant peer-reviewed venues (e.g., CODASPY, Neurips, ICML, ICLR, VLDB, ICDE, SIGMOD, CCS, Usenix Security, NDSS).
• Strong programming and experimental skills.
• Ability to work independently as well as collaboratively in a research group environment.

Preferred Qualifications

• Experience with large language models, trustworthy AI, adversarial machine learning, or privacy-preserving learning.
• Experience with graph neural networks and graph-based data analysis.
• Experience with cybersecurity applications such as intrusion detection, malware analysis, threat intelligence, or security operations.
• Interest in interdisciplinary and applied research with real-world impact.