ECE Tenure-Track Faculty Position in Physical AI

Job Description

The Bradley Department of Electrical and Computer Engineering (ECE) at Virginia Tech invites applications for a tenure-track or tenured faculty position at the assistant or associate professor focusing on Physical Artificial Intelligence (AI). The position is based in Blacksburg, Virginia, with opportunities for collaboration across Virginia Tech's Institute of Advanced Computing (Alexandria, VA) and other university research institutes. The successful candidate will be expected to develop and maintain a nationally recognized funded research program, teach undergraduate and graduate courses, and participate in department, college, and university service and outreach activities.

The ECE department offers B.S., M.Eng., M.S., and Ph.D. degree programs in both Electrical Engineering and Computer Engineering with a current enrollment of approximately 1,300 undergraduate and 670 graduate students. The department has 71 full-time tenured or tenure-track faculty members and 22 non-tenure-track faculty located in two primary locations, which are the Blacksburg Campus and the Greater Washington, DC area including the new VT Institute of Advanced Computing Campus in Alexandria, Virginia.

Annual research expenditures exceed $56M. Recognition of faculty accomplishments includes 4 members of the National Academy of Engineering, 30 Fellows of the IEEE, and various fellows of other professional societies, 27 current and prior NSF CAREER awardees, and 4 DoD Young Investigators. The latest Global Universities ranking by U.S. News & World Report (USN&WR) places our department at #3 nationally in the Electrical and Electronic Engineering category.

The department has some of the nation's best programs in the areas of fiber optics and photonics, space science and remote sensing, wireless communications and networking, power electronics, power systems, autonomous systems, embedded systems, and computational biology. The Department is the beneficiary of the Bradley Endowment valued in excess of $20 million. For additional information about the department and the College of Engineering, please visit (Use the "Apply for this Job" box below).

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We seek a visionary scholar to pioneer the convergence of AI with physical laws, materials, devices, and engineered systems, enabling predictive, trustworthy, and autonomous operation of complex physical platforms. The successful candidate will develop AI-native models, digital twins, and control frameworks that bridge the loop between theory, simulation, experimentation, and deployment across one or more of ECE's core strength areas, including:

* Semiconductors and micro/nanofabrication

* Photonics and optoelectronics

* Quantum and cryogenic devices

* Wireless, communications, networking, and sensing systems

* Power electronics, power systems, and energy infrastructure

This position aligns with national priorities in AI-for-Science, the Genesis Mission, CHIPS and Science Act initiatives, autonomous laboratories, resilient cyber-physical systems, and next-generation infrastructure, and complements Virginia Tech's strong interdisciplinary ecosystem spanning ECE, computing, materials, and applied sciences.

Research Focus

We invite candidates whose research advances Physical AI-AI systems that reason over, learn from, and act within the physical world, grounded in first principles and experimental reality. Areas of interest include, but are not limited to:

* Physics-Informed and Hybrid AI Methods:
Physics-Informed Neural Networks (PINNs), operator learning, and neural surrogates; hybrid modeling combining governing equations, simulations, and data; uncertainty-aware learning, interpretability, and robustness for physical systems; and inverse problems, co-design, and constrained learning under physical laws.

* Digital Twins and Autonomous Physical Systems:
Multi-scale digital twins linking devices, processes, and systems; AI-enabled Design-Build-Test-Learn (DBTL) acceleration; autonomous experimentation, adaptive control, and self-driving laboratories; and secure, reproducible, and standards-aligned twin infrastructures.

Candidates may focus on one or more domains such as:
Semiconductor devices and nanomanufacturing (process control, yield learning, variability, reliability);
Photonics and optoelectronics (inverse design, fabrication-aware modeling, nonlinear or multi-physics systems);
Quantum and cryogenic platforms (noise modeling, calibration, control, materials-device coupling);
Wireless and sensing systems (AI-native PHY/MAC, RF-aware learning, joint sensing-communications);
Power electronics and power systems (physics-aware grid modeling, stability, protection, resilience, microgrids);
Cross-domain work that transfers Physical AI methods across platforms.

This position offers an opportunity to shape the future of computing at Virginia Tech through research, teaching, and service. The candidate will teach core courses in computer engineering-such as embedded systems, computer architecture, and…