We are looking for bright and driven graduates from all disciplines to work on exciting projects in our Analyst team.
You will work across a range of healthcare settings to understand requirements, and then collaborate with Software Developers to design new functionality. The role combines aspects of business analysis, systems analysis, and project management.
AI. Climate change. Two defining challenges. One shared future. How will they transform our world, and how can we shape what comes next?
Join an eight-week interdisciplinary programme on either AI or Climate to explore society’s most pressing challenges through inspiring lectures, practical skills and hands-on projects.
It's the Week 5 Student Bulletin!
We're now over halfway through term – is it too early to start the countdown to Christmas?
Noah has sent us a lovely photo of his two furry friends: Algebra (the grey one) and Trigonometry, or "Trig" for short (the black-and-white one), enjoying the sun and getting a nice tan. How adorable! 🐾
We are currently inviting applications for two Postdoctoral Research Associate positions to work with Professors Jon Keating and James Maynard at the Mathematical Institute, University of Oxford. These are three-year, fixed-term positions, supported by a Simons Collaboration grant funded the Simons Foundation that commences in January 2027. The starting date of the positions is flexible, between January 2027 and October 2027.
Kate Wenqi Zhu is a Postdoctoral Researcher at the Mathematical Institute, University of Oxford, who will give a talk on the topic of ‘AI for Mathematics: open problems, a workflow, and what it leaves behind’.
2026 has felt like a grokking moment for AI and mathematics. This talk is a working report from a numerical analyst, not an AI evangelist. I will describe (1) seven open problems from the numerical linear algebra catalogue that we resolved with AI agents, and how the community has responded; (2) the agent-to-agent workflow behind them, designed so that every proof is interpretable, traceable to its sources, and teachable, with independent cross-model review before anything is recorded as a result; (3) two worked examples: a deterministic eigenvalue-separation result whose proof pieces together convex analysis and classical numerical linear algebra, and a sum-of-squares result whose key idea we distilled into a reusable 'skill' for later problems; and (4) what I think this means for mathematicians: which problems suit AI, which suit people, and why asking the right question matters more than ever. I would like to leave time for an open discussion.