PhD Opening in Physics-Informed Machine Learning, Control, and Optimization at University of Central Florida University of Central Florida in United States

PhD opening in Physics-Informed Machine Learning, Control, and Optimization at the University of Central Florida (UCF), within the Intelligent Cyber-Physical Systems (iCPS) Lab in Electrical and Computer Engineering.

The group is recruiting one motivated PhD student to work on next-generation composable, physics-informed learning methods for complex cyber-physical systems. Research themes include differentiable and modular models for large-scale heterogeneous systems, principled machine learning that integrates physical constraints, data, and domain knowledge, and learning-enabled control and optimization with safety, reliability, and real-world impact.

Possible application areas mentioned in the post include autonomous cyber-physical systems, smart energy systems, robotics, and networked infrastructure. The role appears to suit students who enjoy both theory and implementation: developing new methods, building reusable software frameworks, and translating research ideas into practical tools.

Preferred background: an MS in engineering, computer science, applied math, or a related STEM field; exceptional BS students may also be considered. Applicants should have research experience in machine learning, control, optimization, robotics, or autonomous CPS, with publications or substantial projects preferred. Strong programming and computational skills are important, especially in Python, Julia, differentiable programming, optimization, or scientific computing.

Eligibility note: domestic US students or international students already in the US are especially encouraged to reach out.

Application: the post says the application link is in the comment. Interested candidates should email the recruiter with subject line "PhD EE application" and include a brief intro, relevant background, CV, publications, and supporting documents. Only shortlisted applicants will be contacted.

Funding: no explicit funding details are stated in the post.

Deadline: no deadline is mentioned.