Hongliang Xin

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.

Virginia Tech is recruiting multiple Postdoctoral Associates in the research group of Professor Hongliang Xin for work at the intersection of agentic AI , computational catalysis , machine learning , and autonomous scientific discovery . The positions focus on building AI-native frameworks that combine physics-based modeling, machine-learning methods, knowledge-graph and ontology-based scientific data infrastructures, and agentic workflows for autonomous hypothesis generation, mechanistic exploration, and design of catalytic systems. Research directions include agentic AI for science, computational catalysis and reaction engineering, multiscale modeling, knowledge graphs and scientific data infrastructure, autonomous hypothesis generation and experimental design, and machine learning for catalytic materials discovery. Eligible applicants should hold a Ph.D. in Chemistry, Chemical Engineering, Materials Science, Physics, Computer Science, or a related field. The PhD must have been awarded no more than four years before the appointment date, with at least one year of eligibility remaining. Strong experience in multiscale/multiphysics modeling relevant to catalysis, machine learning models, reaction kinetics, thermodynamics, and structure-reactivity relationships is expected. Experience with agentic AI, automated data curation, workflow orchestration, ML integration, or AI-assisted experimental design is especially relevant. This is a full-time appointment available immediately. The initial term is one year, with possible renewal based on performance and funding. Salary starts at $62,232 and is commensurate with experience. Applications are reviewed on a rolling basis until the positions are filled. To apply, submit a cover letter, CV, contact information for three professional references, and up to three representative publications or preprints through the Virginia Tech application portal.