Graph Machine Learning Postdoc Jobs (NOW HIRING)

Postdoctoral Associate
Job no: 536282
Work type: Research Faculty
Senior management: College of Engineering
Department: Computer Science
Location: Blacksburg, Virginia
Categories: Engineering, Research / Scientific
Job Description
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.
Overtime Status
Exempt: Not eligible for overtime
Appointment Type
Restricted
Salary Information
$63,000-$73,000
Hours per week
40
Review Date
May 20, 2026
Additional Information
The successful candidate will be required to have a criminal conviction check.
About Virginia Tech
Dedicated to its motto, Ut Prosim (That I May Serve), Virginia Tech pushes the boundaries of knowledge by taking a hands-on, transdisciplinary approach to preparing scholars to be leaders and problem-solvers. A comprehensive land-grant institution that enhances the quality of life in Virginia and throughout the world, Virginia Tech is an inclusive community dedicated to knowledge, discovery, and creativity. The university offers more than 280 majors to a diverse enrollment of more than 36,000 undergraduate, graduate, and professional students in eight undergraduate colleges, a school of medicine, a veterinary medicine college, Graduate School, and Honors College. The university has a significant presence across Virginia, including Blacksburg, the greater Washington, D.C. area, the Health Sciences and Technology Campus in Roanoke, sites in Newport News and Richmond, and numerous Extension offices and research institutes. A leading global research institution, Virginia Tech conducts more than $650 million in research annually.
Virginia Tech endorses and encourages participation in professional development opportunities and university shared governance. These valuable contributions to university shared governance provide important representation and perspective, along with opportunities for unique and impactful professional development.
Virginia Tech does not discriminate against employees, students, or applicants on the basis of age, color, disability, sex (including pregnancy), gender, gender identity, gender expression, genetic information, ethnicity or national origin, political affiliation, race, religion, sexual orientation, or military status, or otherwise discriminate against employees or applicants who inquire about, discuss, or disclose their compensation or the compensation of other employees or applicants, or on any other basis protected by law.
If you are an individual with a disability and desire an accommodation, please contact Joseph Morgan at jmorgan99@vt.edu during regular business hours at least 10 business days prior to the event.
Advertised: April 29, 2026
Applications close:
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