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.
IBM Research Europe (UK) seeks exceptional MSc and PhD students for its 2026 summer internship program. Our team pioneers advanced Artificial Intelligence and Machine Learning to accelerate scientific discovery, and the next generation of generative AI.
As an intern, you will collaborate with leading researchers on algorithmic breakthroughs and AI-driven solutions, including:
· Designing novel AI architectures and training algorithms to improve the efficiency, robustness, and scalability of generative models across multi-modal tasks.
· Creating and testing single- and multi-agent systems, introducing new optimization and learning strategies that redefine state-of-the-art performance.
· Investigating innovative techniques in model alignment, adaptive learning, and resource-efficient training for real-world impact.
· Advancing foundation models for urgent global sustainability challenges.
You will help define research challenges, prototype and evaluate solutions, design experiments, and share findings through patent applications and publications in top-tier venues.
To Apply please submit:
· CV
· One-page cover letter highlighting relevant experience, especially in AI, algorithmic innovation and the research themes described above.
· Enrolment in a Master’s or PhD program in computer science, mathematics, statistics, or a related field.
· Strong programming skills (e.g., Python).
· Demonstrated ability to tackle analytical problems with rigorous, quantitative approaches.
· Experience developing, training, and evaluating deep neural networks.
· Expertise with ML frameworks such as PyTorch.
· Research or practical experience with generative models (e.g., large language models, multi-modal foundation models).
· Background in algorithm design or optimization techniques (e.g., novel training objectives, efficient inference strategies).
· Record of clear research communication, evidenced by publications or presentations at premier AI conferences (e.g., NeurIPS, ICLR, ICML, ACL, EMNLP, CVPR).