Artificial Intelligence Placement (12.5 months)

Airbus
Filton, England, United KingdomPosted Sep 7, 2026

About the role

Job Description

Start date: 16 August 2027 Location: Filton, Bristol Duration: 12.5 months Applications close on 27 September 2026! We love your interest in joining Airbus! There is no limit on the number of positions you can apply for, however, please be aware that you can only progress in the selection process for one position at a time. We recommend reviewing all available opportunities and applying to those that best match your experience and aspirations. AI Guidance: We know you might be thinking about using AI to help you in your application - before you do, please read our guidelines on the use of AI for recruitment at Airbus https://www.airbus.com/en/careers/faqs#using-ai-in-your-application

Benefits

Salary: £24,246 35 hours per week // Flexible working hours // Hybrid working options available depending on the role // Half day Fridays! // 25 days holiday plus bank holidays

About us

We are a community of scientists dedicated towards pushing forward the State of the Art in Artificial Intelligence for aerospace. We collaborate with engineers and scientists across Airbus to better understand problems and help solve them. Artificial Intelligence is a booming technology and we aim to harness the benefits across the business, through the entire lifecycle of an aircraft, from the first thoughts to final flight.

What you will be doing

You will be working alongside AI Scientists and Experts to support the team’s research projects, driven by the needs of the projects and your own passion and interest in the topics. Expect to be undertaking research into the latest AI techniques, from reading scientific papers on new breakthroughs, to assisting in the design, development, and execution of experiments to work out the best way of achieving our objectives, keeping our customers needs at the very core. Our UK based team-members have particular interests around AI Explainability (particularly in Reinforcement Learning), Foundation Models for Robotics, Knowledge Repre

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