Openings
We welcome motivated students and researchers interested in computational materials science, first-principles calculations, high-throughput materials discovery, and physics-guided AI.
Computational Materials Group
The Yang Research Group develops and applies first-principles calculations, high-throughput computational workflows, descriptor-based modeling, and physics-guided artificial intelligence to understand and design functional materials. Current research directions include emergent magnetism, spin textures, nonlinear and quantum materials, complex/disordered materials, and AI-guided materials discovery.
Research Opportunities
Prospective undergraduate students with strong self-motivation and interest in computational materials research are welcome to contact Prof. Yang with a resume for consideration.
Students who enjoy problem solving, scientific computing, data analysis, and materials physics are especially encouraged to inquire.
Considering a Ph.D.?
Candidates should have a strong background or serious interest in one or more of the following areas: solid-state physics, solid-state chemistry, inorganic chemistry, materials science, computational science, or applied physics.
Students interested in first-principles simulations, high-throughput calculations, magnetic/quantum/optical materials, and AI-enabled materials discovery are encouraged to apply through the UC San Diego graduate admissions process and contact Prof. Yang.
Collaborative Research
Visiting Ph.D. students and scholars with aligned research interests are welcome to contact the group. Relevant areas include electronic-structure calculations, computational materials design, data-driven materials discovery, and atomistic modeling of functional materials.
Useful Background
- Strong interest in materials research and the discovery of new materials phenomena.
- Background in solid-state physics, chemistry, materials science, nanoengineering, or related fields.
- Comfort with Linux-based computing environments and scientific software.
- Programming experience in Python, MATLAB, C/C++, Fortran, or related languages is valuable.
- Interest in density functional theory, high-performance computing, machine learning, or data-driven materials design is strongly encouraged.
Inquiry Materials
Interested candidates may send a brief email describing research interests, relevant background, and a current CV or resume to kesong@ucsd.edu.