AI for science

Under the theme Aspiration, Action, Anticipation: AI for Science Looking to 2030, this summit asks how AI is already changing scientific practice and how research may develop over the next few years. Researchers will examine scientific reasoning and discovery alongside applications in cancer biology, climate and weather prediction, animal behaviour and cosmology. Talks, discussion and a poster session connect methodological advances with practical scientific problems. Confirmed contributors include Max Welling, Hana Aliee, Boris Bolliet, Cait Newport, Richard Turner, Neil Lawrence, Stephen Rob

…intelligence to advance materials science. The tenure-track appointment is based in Materials Science and Engineering and connected with Berkeley's AI-for-science community.

The Argonne Leadership Computing Facility's Margaret Butler Fellowship in Computational Science is a postdoctoral fellowship for high-impact research at the intersection of AI for science and high-performance computing, aimed at broadening domain science in leadership-class computing. The fellow pursues a self-directed research programme proposed in the application, using ALCF resources such as the Aurora exascale supercomputer and AI accelerators in the AI Testbed, and works with multidisciplinary science teams. The appointment is for one year, renewable for another, with a professional allow

IJCAI invites original research across artificial intelligence, including new problems, methods for new application domains and interdisciplinary work. Alongside its main track, the 2027 edition includes special tracks on AI for science, health, arts and creativity, and social good. A satellite event follows in Hengqin, China, on 15–17 August. Abstracts are due 4 January and papers 11 January 2027, at 23:59 AoE. Main-track submissions allow seven body pages plus two reference pages and use double-blind review. Accepted work requires an author presentation in person. Submission fees and recipr

Fermilab's Computational Science and AI Directorate seeks a postdoctoral researcher in AI for science to join Dr. Aleksandra Ciprijanovic's group, working at the intersection of deep learning, cosmology and high-energy physics on both methodological AI research and scientific applications. Project focus areas are graph neural networks for cosmology, neutrino and/or collider physics; domain adaptation and model robustness; uncertainty quantification; and model interpretability. Experience with simulation-based inference with normalizing flows or flow matching, diffusion models, transformers or