Jiayu Peng

The University at Buffalo is seeking a strong PhD candidate to join the research groups of Prof. Kaihang Shi and Prof. Jiayu Peng. The position focuses on interdisciplinary research at the intersection of artificial intelligence/machine learning (AI/ML), materials science, and chemical engineering. The successful candidate will work on computational methods aimed at accelerating scientific discovery, leveraging data science and advanced computational techniques. This opportunity is part of UB's Computational and Data-Enabled Sciences (CDES) 3-year PhD program, which emphasizes interdisciplinary collaboration and innovative research. Applicants should already possess a Master's degree and demonstrate a keen interest in computational approaches to scientific problems. The program is designed for students excited about working across traditional boundaries in AI/ML, materials science, and chemical engineering. Prospective students are encouraged to mention both Prof. Shi and Prof. Peng in their application materials. For more information about the CDES PhD program, visit the official program page. The application deadline for Fall 2026 is approaching, so interested candidates should act promptly. Funding details are not specified in the announcement; applicants may contact the supervisors or consult the program website for further information. For questions or to discuss research opportunities, candidates are invited to reach out to the supervisors. This is an excellent opportunity for those passionate about computational science and interdisciplinary research in a leading US institution.

A co-advised PhD position is available at the University at Buffalo in the Computational and Data-Enabled Sciences (CDES) PhD program, starting Fall 2026. The opportunity is at the intersection of artificial intelligence, materials science, and chemical engineering, and is supervised by Dr. Jiayu Peng and Dr. Kaihang Shi. The CDES program is interdisciplinary, integrating data science, numerical algorithms, and high-performance computing to accelerate scientific and engineering discovery. Applicants must already hold a Master’s degree and the program is designed for a 3-year completion timeline. Interested candidates are encouraged to contact the supervisors by email to learn more about the research and opportunities, and should mention both supervisors in their statement of purpose and application portal. More information about the program can be found on the university website. Funding details are not specified in the post, so applicants should inquire directly. The University at Buffalo is a leading research institution in the US, offering a collaborative environment for interdisciplinary research.

University at Buffalo is hiring a Postdoctoral Associate in Agentic AI × Computational Catalysis within the Peng Research Group . The position sits at the intersection of scientific AI , first-principles atomistic simulations , computational catalysis , and data-driven materials discovery , with primary support for the DOE Genesis Mission project. The postdoc will also benefit from access to world-class AI computing through Empire AI and collaboration with a multi-institutional DOE team. This opportunity is based in Buffalo, NY and is full-time, in person . The preferred start date is September 2026 or as soon as possible thereafter, and applications are reviewed on a rolling basis . Applicants should hold a PhD in materials science, chemical engineering, chemistry, physics, computational science, or a closely related field. The post specifically seeks experience with DFT or related first-principles simulations, AI/ML for scientific or engineering problems , Python , Linux/HPC environments, and automated, reproducible computational workflows. Backgrounds in GNNs , machine-learning interatomic potentials , active learning/Bayesian optimization , agentic AI , and computational catalysis are especially welcome. To apply, email [email protected] with your CV, at least two references, and a cover letter describing your research experience, interest in the role, and earliest available start date.