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Research Scientist-AI & Algorithmic Innovations Intern

Job in Warrington, Cheshire, MK46, England, UK
Listing for: IBM
Seasonal/Temporary, Apprenticeship/Internship position
Listed on 2026-06-06
Job specializations:
  • Research/Development
    Data Scientist, Artificial Intelligence
  • IT/Tech
    Data Scientist, Artificial Intelligence, AI Engineer, Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 80000 - 100000 GBP Yearly GBP 80000.00 100000.00 YEAR
Job Description & How to Apply Below
Position: Research Scientist-AI & Algorithmic Innovations Intern: 2026

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.

Application requirements
  • One-page cover letter highlighting relevant experience, especially in AI, algorithmic innovation and the research themes described above.
Required technical and professional expertise
  • 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.
Preferred technical and professional experience
  • 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).
Your role and responsibilities
  • 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.
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