Ph.D. Openings | 2026/2027
Join the CODE2 Lab
Fully funded Ph.D. positions in AI for materials, engineering design, and scientific machine learning at Oakland University.
The CODE2 Lab is recruiting fully funded Ph.D. students for 2026/2027 admission at Oakland University.
Research areas
Ph.D. students will develop computational methods in one or more of the lab's core research directions:
- Autonomous design and materials discovery with scientific AI agents
- Generative co-design of materials and mechanical systems
- Uncertainty-aware learning, robust design, and reliability analysis
Projects are primarily computational and may connect simulation and machine learning with experimental data through collaborations.
How to apply
Interested students should email Dr. Zihan Wang at zihanwang@oakland.edu with a brief introduction, research interests, CV, unofficial transcript, and links to relevant projects, publications, or code. Please explain how your background and goals connect with the CODE2 Lab's research. Applicants must also submit all required materials through Oakland University's graduate application portal.
Qualifications
- B.S. or M.S. in mechanical engineering, materials science, computer science, applied mathematics, physics, or a related field
- Programming experience in Python; experience with MATLAB, PyTorch, or other scientific computing tools is helpful
- Foundation in linear algebra, probability and statistics, numerical methods, optimization, or related quantitative topics
- Experience in machine learning, finite element analysis, computational mechanics, materials modeling, or design optimization is a plus
- Strong motivation for research, clear communication, and the ability to work independently and collaboratively
Undergraduate research
Oakland University undergraduates interested in computational research are welcome to contact Dr. Wang. Please include a brief introduction, resume or CV, transcript or GPA, and examples of relevant programming, simulation, or course projects. Helpful preparation includes coursework in linear algebra, differential equations, mechanics or materials, probability and statistics, and programming in Python or MATLAB.
Collaborations
The CODE2 Lab welcomes collaborations with academic researchers, national laboratories, and industry partners. Areas of shared interest include autonomous materials discovery, scientific machine learning, generative design, uncertainty quantification, digital twins, metamaterials, and reliable mechanical systems. Please contact Dr. Wang to discuss potential research partnerships, joint proposals, or student projects.