Jin Huang

The Huang Materials Acceleration and Intelligence Laboratory (MAIL) at the University of Notre Dame is recruiting postdoctoral fellows and also notes PhD student opportunities in related areas. The lab focuses on electrocatalysis , high-throughput materials discovery , AI-guided design , machine learning , and scalable electrochemical systems for sustainable chemical transformations and energy/chemical conversion. Research directions include AI-guided discovery of catalytic materials using megalibraries and high-throughput experimentation, electrocatalysis for sustainable chemical synthesis, advanced electrochemical and photoelectrochemical platforms, and translation of catalyst discovery to scalable systems such as electrolyzers and industrially relevant reactors. Ideal candidates for the postdoctoral role should hold a PhD in Chemical Engineering , Chemistry , Materials Science and Engineering , or a closely related field. The post specifically welcomes applicants with backgrounds in electrochemistry, catalysis, nanomaterials synthesis, high-throughput experimentation, data science, and/or machine learning. The position is a full-time, one-year appointment with the possibility of renewal depending on performance and funding availability. Applications are reviewed immediately, and the preferred start date is October 2026 , with flexibility up to early 2027 . The application deadline is 2026-05-10 . Apply through the Google Form provided in the post. The lab states that applications sent directly by email will not be considered. For additional information, candidates can review the Notre Dame postdoctoral policies page or contact Dr. Jin Huang at [email protected] .

Jin Huang, Assistant Professor of Chemical and Biomolecular Engineering at the University of Notre Dame, is recruiting PhD students for the Materials Acceleration and Intelligence Laboratory (MAIL). The group works at the intersection of materials science , chemical engineering , and chemistry , with research themes including high-throughput accelerated materials discovery, electrocatalysis, scalable electrochemical systems, and AI-driven discovery for energy and chemical conversion. This opportunity is aimed at highly motivated students and researchers who want to contribute to next-generation approaches in a collaborative and fast-growing lab environment. No deadline, funding package, or formal eligibility checklist is stated in the post. Interested applicants should review the lab website for further details and application guidance.