UK AI Hubs Launch National Challenge for Early Career Researchers

Timothy Abrahams on 3 June, 2026

The Erlangen AI Hub is taking part in an exciting new initiative, the AI Hubs National Public Engagement Challenge with Expressions of Interests needed this month (June). The competition offers PhD students and Postdoctoral Researchers within the UKRI AI community a chance to present their work in a compelling three-minute video designed for a public audience, and to showcase the depth and diversity of UK AI research on a national stage.

For early career researchers looking to strengthen their communication profile and raise the visibility of their work, this is an excellent opportunity. Participants will:

  • Build valuable public engagement skills essential for grant applications and careers.
  • Receive expert training on how to translate complex research into engaging stories.
  • Connect with peers across the UK AI Hubs, expanding their network.
  • Gain visibility across the UK AI ecosystem.
  • Compete for a spot in the national final, where the top five videos will be presented at the UKAIRS 2026 conference dinner in November, before an audience of policymakers, funders, industry leaders and researchers.
  • Potentially take home the title of Overall Winner.

The challenge is open to researchers connected to a UK AI Hub through any of the following:

  • Postdoctoral researchers employed on a UKRI AI Research Hub
  • PhD students within a UKRI AI CDT or another Doctoral Training Programme aligned to AI research
  • PhD students or PDRAs within the research group of an AI Hub Co Investigator, Partner or Member

If you are connected to a UK AI Hub, this is your chance to get involved. Eligible researchers must submit an Expression of Interest by 12 June 2026 including:

  • Name
  • Role: PhD Student or Postdoctoral Research Associate
  • Name of CDT or AI Research Hub
  • A 300-word description of their research for a general audience
  • A 150-word statement on why they want to take part

Submit your Expression of Interest by email by 12 June 2026 to info@aichemy.ac.uk with the subject line: ‘AI Hubs: National Public Engagement Challenge’

Shortlisting will take place on 24 June 2026, with 20–30 researchers selected to progress. Selected researchers will attend an online training session on presenting to public audiences and structuring an effective three-minute pitch. The session will also offer opportunities to practise with peers from other Hubs. Participants will then create their own three-minute video, using one slide (animation permitted). The final criteria for judging will be confirmed beforehand. A panel of external reviewers, including EPSRC Project Officers, will select the five finalists. All videos will be shared online to highlight the breadth of AI research across the UK.

The five finalists will be invited to attend UKAIRS 2026 (to be held at John McIntyre Conference Centre, Edinburgh above) on 24–25 November and their videos will be showcased at the conference dinner. A judging panel external to the Hubs covering policy and communication specialists will select the overall winner. If you are an early career researcher associated with a UK AI Hub, don’t miss this chance to share your work, develop your skills, and connect with the wider AI community.

Start preparing your Expression of Interest and bring your research to life in three minutes!

First speakers announced for conference

Timothy Abrahams on 28 May, 2026

Some exceptional speakers for the Mathematical Foundations of AI Conference 2026 have been announced. These voices bring together world-leading expertise at the intersection of mathematics, machine learning, artificial intelligence, geometry and topology. From pioneers of geometric deep learning and graph neural networks to leading researchers in topology, optimisation, formal proof verification, and relational AI, the conference will showcase cutting-edge ideas shaping the future of intelligent systems.

By the very nature of their wide-ranging contributions the following biographies have been edited for brevity.

Erik Bekkers
Erik Bekkers is an associate professor in Geometric Deep Learning in the Machine Learning Lab of the University of Amsterdam. His work emanates from the belief that as nearly all data is rooted in our physical world it is thus inherently grounded in geometry and physics and representation learning should preserve this grounding. His current research focuses on developing generalizations and efficient implementations of group equivariant architectures. 

Michael Bronstein
The Erlangen AI Hub founder and co-director is a pioneer of geometric deep learning and graph neural networks. In addition, Michael Bronstein is DeepMind Professor of AI at the University of Oxford, Erlangen AI Hub Director and Founding Scientific Director of AI at AITHYRA. His work bridges academia and industry, shaping the future of non-Euclidean machine learning and AI innovation.

Kathryn Hess
Kathryn Hess is a leading mathematician known for her work in homotopy theory, category theory, and algebraic topology, as well as for applying topology to neuroscience, cancer biology, and materials science. Professor at École Polytechnique Fédérale de Lausanne, she is also a member of the Swiss Academy of Engineering Sciences, a Fellow of the American Mathematical Society and the Association for Women in Mathematics.

Kevin Buzzard
Professor of Pure Mathematics at Imperial, Kevin Buzzard’s Strachey Lecture in 2025: Will Computers prove theorems? explored how mathematicians and computer scientists can collaborate to take AIs contribution to the field further, exploring the conservative nature of mathematics, slow to change and adapt, can adopt tools like language models and theorem provers to accelerate the field.  

Stefanie Jegelka
Professor at MIT and TU Munich, Stefanie Jagielka’s research investigates the combinatorial, geometric, and algebraic foundations of machine learning. This includes learning with discrete objects, such as graphs or sets; learning with symmetries; discrete probability; learning with limited supervision, and the interplay of discrete and continuous optimization. She has collaborated with researchers in biology, materials science, ocean engineering and power grids.

Suvrit Sra
Suvrit Sra is Career Development Associate Professor of EECS MIT and Professor for Resource Aware Machine Learning at TU Munich. He specializes in robust, reliable, and resource-efficient machine learning methods. His research focuses, in particular, on solving optimisation problems for machine learning with multiple parameters. For example, those that are used in autonomous driving so that a car can reliably distinguish a sign from a person.

Dr. Rebekka Burkholz
A faculty member at the CISPA Helmholtz Center for Information Security, Rebekka Burkholz researches relational machine learning. Her main goal is to gain a theoretical understanding of deep learning from a complex network perspective and improve contemporary algorithms based on these insights. Her current applications focus on molecular biology.

Roland Kwitt
Professor of Machine Learning within the Department of Artificial Intelligence and Human Interfaces (AIHI) at the University of Salzburg (PLUS), Roland Kwitt studies learning methods that exploit the structural characteristics of complex data. With experience in medical imaging and computer vision, his work connects theoretical machine learning with impactful real-world applications.

Register now to reserve your place at the conference and follow us for further speaker announcements over the coming weeks.