Postdoc In Topological Data Jobs in Virginia (NOW HIRING)

Postdoctoral Associate
Job no: 536321
Work type: Research Faculty
Senior management: College of Engineering
Department: Chemical Engineering
Location: Blacksburg, Virginia
Categories: Engineering, Research / Scientific
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.
Overtime Status
Exempt: Not eligible for overtime
Appointment Type
Restricted
Salary Information
Starting at $62,232; commensurate with experience
Hours per week
40
Review Date
05/18/2026
Additional Information
The successful candidate will be required to have a criminal conviction check.
About Virginia Tech
Dedicated to its motto, Ut Prosim (That I May Serve), Virginia Tech pushes the boundaries of knowledge by taking a hands-on, transdisciplinary approach to preparing scholars to be leaders and problem-solvers. A comprehensive land-grant institution that enhances the quality of life in Virginia and throughout the world, Virginia Tech is an inclusive community dedicated to knowledge, discovery, and creativity. The university offers more than 280 majors to a diverse enrollment of more than 36,000 undergraduate, graduate, and professional students in eight undergraduate colleges, a school of medicine, a veterinary medicine college, Graduate School, and Honors College. The university has a significant presence across Virginia, including Blacksburg, the greater Washington, D.C. area, the Health Sciences and Technology Campus in Roanoke, sites in Newport News and Richmond, and numerous Extension offices and research institutes. A leading global research institution, Virginia Tech conducts more than $650 million in research annually.
Virginia Tech endorses and encourages participation in professional development opportunities and university shared governance. These valuable contributions to university shared governance provide important representation and perspective, along with opportunities for unique and impactful professional development.
Virginia Tech does not discriminate against employees, students, or applicants on the basis of age, color, disability, sex (including pregnancy), gender, gender identity, gender expression, genetic information, ethnicity or national origin, political affiliation, race, religion, sexual orientation, or military status, or otherwise discriminate against employees or applicants who inquire about, discuss, or disclose their compensation or the compensation of other employees or applicants, or on any other basis protected by law.
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Advertised: May 13, 2026
Applications close:
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