Introduction IBM Research takes responsibility for technology and its role in society. Working in IBM Research means you'll join a team who invent what's next in computing, always choosing the big, urgent and mind-bending work that endures and shapes generations. Our passion for discovery, and excitement for defining the future of tech, is what builds our strong culture around solving problems for clients and seeing the real world impact that you can make. IBM's product and technology landscape includes Research, Software, and Infrastructure. Entering this domain positions you at the heart of IBM, where growth and innovation thrive. 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 experience - 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.