Fully Funded PhD in Scientific Machine Learning, Computational Physics, and Fluid Dynamics at Southern Methodist University Southern Methodist University in United States

The Artificial Intelligence for Design (AI4D) Lab at Southern Methodist University (SMU) is offering a fully funded PhD position starting in Spring or Fall 2026. The research focus is on scientific machine learning, computational physics, and large-scale computing, with applications in design and optimization of complex systems, particularly in fluid dynamics. The successful candidate will join a dynamic research group in the Lyle School of Engineering, working on cutting-edge topics such as AI/ML, reduced order modeling (ROM), and computational methods for engineering design.

Applicants should have a strong background in engineering, physics, or applied mathematics, with hands-on experience in artificial intelligence, machine learning, and computational methods. Proficiency in programming languages such as Python, Julia, or C++ is required, along with a solid understanding of fluid dynamics, aerodynamics, and turbulence. The position is fully funded, covering tuition, health insurance, and a stipend, making it an excellent opportunity for motivated students interested in advanced computational research.

The AI4D Lab is part of SMU's Department of Mechanical Engineering, located in Dallas, Texas. SMU is recognized for its high-performance computing centers and strong research programs in computational and AI/ML fields. The university is ranked among the top institutions in Texas and the US, providing a vibrant academic environment and excellent career prospects for graduates.

To apply, candidates should contact Dr. Hamid R. Karbasian by email ([email protected]) with the subject 'PhD Application - SML', including their most updated CV, unofficial transcripts, and a short cover letter outlining their objectives. Applicants must also meet the general admission requirements of the Lyle School of Engineering.

Key research areas: scientific machine learning, computational physics, fluid dynamics, large-scale computing, optimization, AI4D Lab, mechanical engineering.