Topic: AI for science

Paco Villaescusa surveys the growing role of numerical simulations and AI in cosmology. Quijote and Backlight explore how much information about the Universe can be recovered beyond conventional summary statistics, including nonlinear structure and higher-order relationships. CAMELS addresses uncertainty in galaxy formation and other astrophysical processes, while DREAMS examines dark-matter predictions in the presence of baryonic effects. The seminar then considers automated research workflows through Denario, a system that develops hypotheses, plans analyses, writes and executes code, and i

The University of Cambridge's Handley Lab seeks a researcher to connect fundamental theories with cosmological and astrophysical observations using AI-assisted scientific software and GPU-accelerated inference. The role includes developing physical predictions, Bayesian methods including nested sampling, publishing research and shaping an independent research programme. The post is based at the Kavli Institute for Cosmology and Institute of Astronomy, with funding initially available until 30 September 2030. Appointment is at Grade 7 or Grade 9 according to experience; Grade 9 requires Faculty

ISTA Lecture by Klaus Robert Müller (TU Berlin & Korea University, Seoul) on how machine learning and AI enable scientific research, particularly in medicine and chemistry, and on explainability techniques for extracting understanding from machine learning models.

RIKEN invites junior researchers with advanced mathematical, computational or information-technology skills to support research across its host laboratories, including AI for Science. Around five appointments are planned. Fellows primarily join the Research Engineer Enablement Office in Wako and also work with a host laboratory; annual contracts may be renewed for a maximum of three years. Applicants must first secure a prospective host’s agreement. Mandatory registration closes on 23 October 2026 at 17:00 JST; documents and two reference letters are due on 30 October at 14:00 JST. The listed

Collaborate with interdisciplinary teams to develop infrastructure for materials science experiments and apply AI/machine learning to accelerate discovery of new functional materials through computational simulation and automated experimentation.