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Old Dominion University

Faculty in Data Sciences - Critical Infrastructure and Data Transformation (CID) to Advance National Security

🇺🇸 Norfolk, VA 🕑 Full-Time 💰 TBD 💻 Data Science 🗓️ February 11th, 2026
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Edtech.com's Summary

Old Dominion University is hiring a Faculty member in Data Sciences focused on Critical Infrastructure and Data Transformation to Advance National Security. The position entails developing and maintaining an externally funded interdisciplinary research program in AI and data science applied to critical infrastructure and national security challenges, teaching undergraduate and graduate courses, and collaborating with faculty across multiple departments and external research institutions.

Highlights

  • Develop and maintain interdisciplinary research in AI/ML and data science for critical infrastructure and national security.

  • Focus research areas include AI-enabled digital twins, edge intelligence, secure AI, resilience forecasting, human-AI decision support, and cyber-physical threat detection.

  • Teach undergraduate and graduate courses within the School of Data Science and collaborate with other university units.

  • Collaborate with external research entities such as Jefferson Lab, NASA Langley Research Center, Navy Surface Warfare Center, and Brock Virginia Health Sciences.

  • Required education: Ph.D. or equivalent in Data Science, Computer Science, Machine Learning, Engineering, Mathematics, or closely related field.

  • Preference for candidates with strong publication records and externally funded grants in data sciences, AI, or ML.

  • Academic ranks considered: Assistant, Associate, or Full Professor; senior candidates must meet tenure criteria.

  • Appointment is full-time, 10-month annually, starting July 25, 2026.

  • Position supports Old Dominion University's Strategic Plan focusing on Coastal Resilience and National Security.

  • Applicants must submit a cover letter, CV, research statement, teaching philosophy, scholarly work samples, transcripts, and professional references.

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