Junichiro Shiomi

The Thermal Energy Engineering Lab at The University of Tokyo is recruiting three postdoctoral researchers for the JST ARiSE AI for Science project, “Upstream AI: Beyond Foundation Models through Domain-Specific Materials Intelligence – Toward Thermal Management Materials Innovation.” The project develops new artificial-intelligence methods for discovering and designing thermal management materials. Its primary target is thermal properties, while also considering the mechanical, electrical and dielectric characteristics needed for practical applications. Researchers will contribute to materials foundation models and machine-learning interatomic potentials for thermal and related properties. The work also includes AI agents that autonomously connect prediction, first-principles calculation and evaluation; large-scale generation of anharmonic phonon data; automated computational workflows; machine-learning prediction of thermal conductivity; and international benchmarking. The project combines computational materials science with experimental validation. AI-predicted materials will be tested through high-throughput synthesis at partner institutions and precision thermal measurements. Relevant research builds on automated first-principles workflows, anharmonic phonon-property databases, lattice thermal-conductivity surrogates, community benchmarking, human–AI collaborative nanostructure design and robotic experiment-in-the-loop optimization. The researchers will be employed by the University of Tokyo and undertake research stays of several months to approximately one year with partner groups in Singapore and the United States, including collaborators at Nanyang Technological University, the National University of Singapore, the University of California, Berkeley, Northwestern University and the University of California, Davis. The position offers an international research environment spanning AI for Science, machine learning, phonons, thermal transport, first-principles computation, materials synthesis and precision measurement.