Research Intern—Extreme Behaviour Modelling in Large AI Models

Introduction
IBM Research Scientists are charting the future of Artificial Intelligence creating breakthroughs in quantum computing discovering how blockchain will reshape the enterprise and much more. Join a team that is dedicated to applying science to some of today’s most complex challenges whether it’s discovering a new way for doctors to help patients teaming with environmentalists to clean up our waterways or enabling retailers to personalize customer service.

Your Role and Responsibilities
IBM Research Europe (UK) is seeking outstanding doctoral students in computational science to join our 2025 summer internship program. You will join the project “Modelling rare extreme behaviour in large-scale computational models” funded by a UKRI Future Leaders Fellowship. The project leverages a broad collaboration network within IBM Research the UKRI-FLF ecosystem and beyond.

During the internship you will work in a small group to develop explore and characterize the properties of a given computational model. Job responsibilities may include developing and scaling up an existing application exploring model configurations reading relevant literature and communicating findings through patent applications and publications in top-tier conferences.

You should enclose a CV and include a one-page cover letter in your application. Internships are for three months and are based at our Daresbury (Warrington) facilities.

Required Technical and Professional Expertise

• Candidates must be enrolled in a PhD program in the physical mathematical or natural sciences.
• Fluency in developing and running HPC-scale models in the candidate’s own discipline.
• Demonstrated experience in solving analytical problems using rigorous and quantitative approaches.

Preferred Technical and Professional Expertise

• All application domains are of interest but a background in environmental or theoretical physics will be preferred.
• Experience with model-reduction and model-emulation approaches including Physics-Informed Machine Learning.
• Familiarity with non-linear optimization techniques.
• Being able to clearly and effectively communicate research ideas as demonstrated by publications and presentations in the top-tier journals and conferences.