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://apptrkr.com/9801692"">https://www.ubjobs.buffalo.edu/postings/64174
Job Type: Full-Time
Posting Detail Information
Position Summary
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:
As an Equal Opportunity / Affirmative Action 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.
Minimum Qualifications
- 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.
Preferred Qualifications
- 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.
Physical Demands
Salary Range
$60,000
Special Instructions Summary
Is a background check required for this posting?
No
Contact Information
Contact's Name: Melissa King
Contact's Pronouns:
Contact's Title: Financial Transaction Specialist
Contact's Email: mking23@buffalo.edu
Contact's Phone: 716-645-4621
Posting Dates
Posted: 09/02/2026
Deadline for Applicants:
Date to be filled:
je-f45e4909e8624362ade616f2792c2072
