The research group of Professor Hongliang Xin at Virginia Tech is seeking a Postdoctoral Associate to advance agentic AI systems for catalysis. This position focuses on the intersection of computational catalysis, experimental design, and autonomous scientific discovery, leveraging first-principles modeling, machine learning, knowledge-graph and ontology-driven scientific data infrastructures, and AI-native agentic workflows. The successful candidate will contribute to building frameworks for autonomous hypothesis generation, mechanistic exploration, and the design of catalytic systems, collaborating with interdisciplinary teams in materials discovery.

Applicants should have a Ph.D. in Chemistry, Chemical Engineering, Materials Science, Physics, Computer Science, or a related field, with strong expertise in first-principles modeling or molecular simulations relevant to catalysis. Experience with machine learning, agentic AI workflows, and a deep understanding of reaction kinetics, thermodynamics, and structure-reactivity relationships in catalytic systems are required. The position values demonstrated experience with agentic AI, including automated data curation, ML model integration, workflow orchestration, or AI-assisted experimental design. Candidates must be able to conduct independent research, collaborate across disciplines, and publish high-quality scientific work.

The position is full-time, with salary commensurate with experience and benefits according to Virginia Tech policy. The appointment is restricted and requires a criminal conviction check. The application review begins January 2, 2026, and the position remains open until filled. Interested candidates should apply via the official application link and may contact Professor Hongliang Xin for further information.

Key research areas include agentic AI, catalysis, computational chemistry, machine learning, materials science, first-principles modeling, molecular simulations, knowledge graphs, ontology, and autonomous scientific discovery. The position is based in the Department of Chemical Engineering at Virginia Tech, a leading research institution in the United States.