University of Southampton
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PhD in Advanced Machine Learning for Helicopter Flight Test Data Analytics University of Southampton in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
Full funding availableDeadline
Aug 25, 2026
Country
United Kingdom
University
University of Southampton

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About this position
PhD opportunity at the University of Southampton in Advanced Machine Learning Framework for Helicopter Flight Test Data Analytics.
This project sits at the intersection of Aerospace Engineering, Computer Science, Mechanical Engineering, and Engineering Mathematics. The research focuses on turning large, heterogeneous helicopter flight-test datasets into a structured, queryable, and predictive system using machine learning and artificial intelligence.
Key topics include automated data preprocessing, synchronization of multi-source onboard signals, unsupervised and supervised learning for manoeuvre cataloguing, and engineering models for power requirements, trim maps, vibrations, and fuel consumption. The project also emphasizes traceability, reproducibility, and interpretable methods rather than black-box modelling, with integration into existing Leonardo Helicopter Division workflows.
Supervisory team: Prof. Andrea Da Ronch and Prof. Simon Cox.
Funding: competition-funded PhD project; tuition fees are covered and a tax-free living stipend is provided.
Eligibility: applicants should hold a UK 2:1 honours degree or international equivalent. An English language qualification is required if applicable.
How to apply: submit an online application, choose Research, 2026/27, Faculty of Engineering and Physical Sciences, select Full time, and search for PhD Engineering & the Environment (7175). Add the supervisor name in section 2 and include a CV, two academic references, transcripts/certificates, and English language evidence if needed.
Deadline: 2026-08-25.
Funding details
Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.
How to apply
Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.
More information can be found here
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