ECE Tenure-Track Faculty Position in Physical AI

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.

• 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.

Required Qualifications

• Ph.D. (by start date) in Electrical and Computer Engineering or a closely related field.
• Demonstrated research potential or accomplishments in Physical AI, Physics-Informed AI, or AI for physical systems.
• Strong grounding in physical modeling, devices, systems, or experimentation relevant to ECE.
• Evidence of potential to secure competitive extramural funding.
• Commitment to excellence in teaching, mentoring, and inclusive academic practices.