Postdoctoral Associate

Job Description

The research group of Professor Hongliang Xin (xingroup.org) at Virginia Tech invites applications for multiple full-time Postdoctoral Associate positions to drive the development of agentic AI systems for accelerating computational catalysis and experimental design. The successful candidate will contribute to 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.

Candidates will collaborate closely with interdisciplinary research groups advancing materials discovery through the convergence of computational chemistry, machine learning, and agentic science.

This full-time appointment is available immediately. The initial appointment will be for one year, with the possibility of renewal based on performance and funding. There is no fixed application deadline, as applications will be reviewed on a rolling basis until the positions are filled. Interested candidates should submit a cover letter detailing their research experience and motivation for the position, a detailed curriculum vitae (CV), contact information for three professional references, and up to three representative publications or preprints.

We look forward to welcoming a dedicated researcher to our team at Virginia Tech, where you will contribute to pioneering research at the intersection of computational materials science and machine learning.

Required Qualifications

• Ph.D. in Chemistry, Chemical Engineering, Materials Science, Physics, Computer Science, or a related field. PhD must be awarded no more than four years prior to the effective date of appointment with a minimum of one year eligibility remaining.
• Strong expertise in multiscale/multiphysics modeling relevant to catalysis, and experience with machine learning models.
• Deep understanding of reaction kinetics, thermodynamics, and structure-reactivity relationships in catalytic systems.
• Demonstrated experience with agentic AI, including automated data curation, ML model integration, workflow orchestration, or AI-assisted experimental design.
• Proven ability to conduct independent research, collaborate across disciplines, and publish high-quality scientific work.