Postdoctoral Associate, Materials Design and Innovation

Postdoctoral Associate, Materials Design and Innovation Position Information

Position Title: Postdoctoral Associate, Materials Design and Innovation

Department: Department of Materials Design and Innovation

Posting Link:  https://(Use the "Apply for this Job" box below)./64174

Job Type: Full-Time

The Department of Materials Design and Innovation at the University at Buffalo invites applications for a full-time Postdoctoral Associate in the Peng Research Group. The successful candidate will conduct research at the intersection of scientific AI, atomistic simulation, computational catalysis, and data-driven materials discovery.

• The position will primarily support a U.S. Department of Energy Genesis Mission project developing closed-loop and agentic AI workflows for chemical-manufacturing research.
• The associate will also contribute to an American Chemical Society Petroleum Research Fund project focused on the data-driven discovery of intermetallic alloy catalysts with unusual electronic structures.
• The research will combine first-principles atomistic simulations, scientific machine learning, reaction modeling, and integration of computational and experimental data.
• The associate will develop reproducible computational workflows, collaborate with researchers across multiple institutions and disciplines, and communicate research findings through publications, presentations, technical reports, software, and research datasets.

Learn more:

• Our benefits, where we prioritize your well-being and success to enhance every aspect of your life.
• Being a part of the University at Buffalo community.

As an Equal Opportunity / Affidavit Employment employer, the Research Foundation will not discriminate in its employment practices due to an applicant's race, color, religion, sex, sexual orientation, gender identity, national origin and veteran or disability status.

• Doctoral degree in materials science and engineering, chemical engineering, chemistry, physics, computational science, or a closely related field. All degree requirements, including the dissertation, must be completed by the date of appointment.
• Demonstrated research experience with density functional theory or closely related first-principles atomistic simulation methods.
• Demonstrated research experience applying artificial intelligence or machine learning to scientific or engineering problems, including the development, training, validation, or evaluation of machine-learning models.
• Proficiency in Python and experience working in Linux and high-performance computing environments.
• Experience developing or using automated and reproducible computational research workflows.
• Ability to conduct computational research independently and analyze and interpret research data.
• Strong written and oral communication skills and the ability to work effectively in a collaborative research environment.

• Experience with graph neural networks, machine-learning interatomic potentials, or related scientific machine-learning methods for atomistic systems.
• Familiarity with uncertainty quantification, active learning, Bayesian optimization, agentic AI, or closed-loop materials discovery.
• Experience in computational heterogeneous catalysis, electrocatalysis, surface science, electronic-structure analysis, or alloy materials.
• Experience with high-throughput density functional theory calculations, adsorption energies, reaction pathways, transition-state calculations, or microkinetic modeling.
• Experience with scientific software development, version control, research data management, and collaborative code development.

$60,000

No

Contact's Name: Melissa King

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Posted: 09/02/2026