Fully Funded PhD in Mechanical Engineering: AI-Enabled Digital Twins, Optimization, and Control Co-Design Texas State University in United States

Fully funded PhD opportunities are available in Mechanical Engineering at Texas State University with Dr. Ying-Kuan (Rick) Tsai, who will join as an Assistant Professor in Fall 2026.

The research group focuses on AI-enabled digital twins, machine learning, optimization, control co-design, reinforcement learning, and uncertainty quantification, with applications in advanced manufacturing, autonomous systems, robotics, aerospace, and smart materials.

Students will work at the intersection of AI/ML, digital twins, design optimization, control systems, and data-driven modeling for dynamic engineering systems. The post highlights topics such as real-time decision-making, model predictive control, system-of-systems digital twins, federated learning, and physics-based simulation.

Funding: the positions are described as fully funded and include a tuition waiver, monthly stipend, and insurance.

Eligibility: applicants should have a B.S. or M.S. in Mechanical Engineering, Computer Science, Aerospace, Applied Mathematics, or a related field, along with a strong academic record and good written and verbal English communication skills. Preferred experience includes PyTorch, TensorFlow, JAX, scikit-learn, Python, MATLAB, C++, control systems, optimization, dynamics, and academic publishing.

Application window: start dates are listed for Fall 2026, Spring 2027, or Fall 2027. Applications are reviewed on a rolling basis until the positions are filled.

How to apply: email the PI with a CV/resume, a one-page summary of research experience and interests, and unofficial transcripts.