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Ying-Kuan Tsai

3 months ago

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

Degree Level

PhD

Field of study

Computer Science

Funding

Two fully funded PhD positions are offered. Funding includes tuition waiver, monthly stipend, and insurance provided.

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Country

United States

University

Texas State University

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Keywords

Computer Science
Machine Learning
Mechanical Engineering
Materials Science
Aerospace Engineering
Uncertainty Analysis
Reinforcement Learning
Digital Twin Technology
Optimisation
Robotics
Autonomous System
Smart Material
ML

About this position

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.

Funding details

Two fully funded PhD positions are offered. Funding includes tuition waiver, monthly stipend, and insurance provided.

What's required

Applicants should have a B.S. or M.S. in Mechanical Engineering, Computer Science, Aerospace, Applied Mathematics, or a related field. Strong academic record and genuine motivation for research are required. Good written and verbal English communication skills are expected. Preferred qualifications include experience with machine learning or deep learning (PyTorch, TensorFlow, JAX, scikit-learn), background in control systems, optimization, or dynamics (MPC, LQR, reinforcement learning), familiarity with digital twin concepts, physics-based simulation, or data-driven modeling, programming experience in Python, MATLAB, C++, or similar tools, and experience writing and publishing academic papers.

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