Jiang Group | Computational Materials and Product Design

About the Jiang Group

We develop physics-informed machine learning and AI methods to understand, predict, and design soft materials and chemical products for energy and sustainability.

These materials pose a multiscale design problem: molecular structure, processing conditions, and product performance are tightly coupled. We build predictive models that bridge these scales, learning from simulation, theory, and data while respecting physical constraints.

Our approach integrates three capabilities:

• Machine learning and AI methodology — physics-informed neural networks, geometric and topological deep learning, and generative models
• Molecular simulation — connecting molecular structure, dynamics, and interactions to macroscale properties
• Materials and product design — translating predictive understanding into materials that are high‑performing, synthesizable, stable, and scalable

Join Us

We are recruiting two PhD students (start: Fall 2026 and Spring 2027) and one postdoctoral researcher to work on AI‑driven soft materials design.

If you’re drawn to problems at the intersection of machine learning, molecular simulation, and theory, we’d be glad to hear from you.

See the openings page for full details and application instructions, or email sjiang87@shenglijiang.com directly.