Fully Funded PhD Positions in Behavioral Machine Learning & Systems, Industrial and Systems Engineering

The University of Houston's Cullen College of Engineering is offering multiple fully funded Ph.D. opportunities in the Department of Industrial and Systems Engineering, starting Fall 2026, Spring 2027, or Fall 2027. These positions are within the Behavioral Machine Learning & Systems Lab, led by incoming Assistant Professor Ekin Uğurel. The lab focuses on developing learning methods for engineered systems where human and AI decision-makers interact, with research topics including incentives, information, and algorithms in transportation, logistics, energy, and other large-scale infrastructure systems. Key research avenues include modeling feedback dynamics in human-AI interactions, developing robust and uncertainty-aware machine learning methods for demand environments, and learning system representations from sparse or biased mobility data. The lab is particularly interested in candidates with backgrounds in civil engineering, industrial engineering, computer science, applied mathematics, statistics, economics, geography, or urban planning. Proficiency in object-oriented programming and computational tools is required, along with a strong interest in operations research, machine learning, statistical learning, travel behavior, or supply chain and logistics. Accepted Ph.D. students will receive competitive financial support, full tuition remission, and comprehensive health insurance. The University of Houston is a flagship R1 institution recognized for its strengths in energy, manufacturing, healthcare systems, and advanced analytics, with strong industry partnerships and a vibrant research environment. To apply, prospective students should email [email protected] with the subject 'Prospective Student – [YOUR NAME]' and attach their CV, transcript, and a brief research statement. Applications are also accepted through the regular application cycle for Fall 2027. This is an excellent opportunity for students interested in behavioral machine learning, systems engineering, and interdisciplinary research at a leading U.S. institution.