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Senior Faculty-Level or Equivalent Position - Secure AI and Agentic Systems

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Senior Faculty-Level or Equivalent Position - Secure AI and Agentic Systems

Category: Faculty
Department: School of Computing, Watson College
Locations: Binghamton, NY
Position ID: 205592

The School of Computing in the Thomas J. Watson College of Engineering and Applied Science at Binghamton University (The State University of New York at Binghamton) invites applications for a senior faculty-level or equivalent position, with an expected start date of September 1, 2027. The primary focus of this search is on established academic researchers for appointment at the tenured Associate Professor or Full Professor level, as appropriate. Exceptionally accomplished senior research and technical leaders from industry, government, or research laboratories will also be considered for an appropriate appointment.

We seek an established scholar and research leader with an outstanding record of externally sponsored research and scholarly impact in the broad area of secure, trustworthy, and agentic artificial intelligence. We are particularly interested in candidates whose research addresses the security, privacy, and complex dynamics of AI systems, from underlying hardware and computational infrastructure through deployment and operation.

The ideal candidate will develop rigorous approaches to securing the AI lifecycle?from silicon to deployment?and to understanding, managing, and mitigating the behavior of interacting AI systems. Research in this area may draw upon hardware, software, cryptographic, formal, and systems-level methods to establish verifiable trust to strengthen privacy guarantees and improve the security and reliability of AI systems. Areas of interest include, but are not limited to:

  • Hardware Security, Privacy, and Trustworthy AI Infrastructure: Developing architectures, protocols, and verification mechanisms that make AI systems trustworthy and resilient against adversarial attacks. Relevant research may include privacy-preserving federated learning, model-weights chain of custody, silicon-rooted inference trust, forensic designs for hardware and systems, and side-channel attacks on GPU inference.

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