Postdoctoral Associate, Materials Design and Innovation

University at Buffalo; Buffalo (NY); On-site; USD 54,000 - 66,000; Full time

Job summary

University at Buffalo seeks a Postdoctoral Associate in the Materials Design and Innovation group to advance AI-driven materials discovery. The role focuses on integrating first-principles simulations with machine learning and data-driven methods, and to build reproducible computational workflows.

The successful candidate will contribute to DOE Genesis Mission and ACS PRF projects, and communicate results through publications and software.

Qualifications

• PhD in relevant field completed by appointment date.
• Experience applying AI/ML to scientific or engineering problems.
• Proficiency in Python and Linux; experience in HPC environments.
• Experience developing automated and reproducible computational workflows.
• Strong communication and collaboration skills.

Responsibilities

• Conduct research at the intersection of AI, atomistic simulation, and data-driven materials discovery.
• Develop reproducible computational workflows and publish results.
• Collaborate across institutions and disciplines; communicate findings in papers and reports.
• Contribute to DOE Genesis Mission and PRF projects.

Skills

Python

Linux

Machine learning

HPC

Education

PhD in materials science and engineering

Job description

Postdoctoral Associate, Materials Design and Innovation

Position Title Postdoctoral Associate, Materials Design and Innovation

Classification Title

Classification Title Postdoctoral Associate

Department

Department Department of Materials Design and Innovation

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.

• Our benefits , where we prioritize your well-being and success to enhance every aspect of your life.

The Research Foundation of SUNY is an EOE and welcomes all to apply including veterans and individuals with a disability.

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.

Our University Community

Physical Demands

Driving Requirements

Driving Requirements This position will never drive a university owned vehicle.

Salary Range

Salary Range $60,000

Additional Salary Information

Additional Salary Information The salary range reflects our good faith and reasonable estimate of the possible compensation at the time of posting, the role and associated responsibilities, and the experience, education, and training of the selected candidate.

Work Hours

Work Hours 37.5 hours per week

Campus

Campus North Campus

Is a background check required for this posting?

Is a background check required for this posting? No

UB is an AA/EOE and welcomes all to apply including veterans and individuals with disabilities. Arrangements will be made if an applicant has a disability in order to provide access to the application, interview and selection process upon request. Reasonable accommodation requests must be made in a timely manner by email at [e-mail] or by phone [phone].