AU
Data-driven polarimetric shape sensing of composite laminates
✓ Fully Funded
⏰ Closing Soon
🎓 Data Analysis
🎓 Data Science
🎓 Engineering
🎓 Optical Physics
machine learning
renewable energy
photonic sensing
aerospace
composite laminates
defect detection
fibre Bragg grating
optical frequency comb
shape sensing
structural health monitoring
This PhD develops an AI-driven polarimetric dual optical frequency comb (DOFC) technique for high-resolution shape sensing and defect detection in composite laminates used in aerospace and renewable energy applications. The research integrates advanced photonic sensing with machine learning to improve defect localisation, classification, and predictive modelling
Project Description
Structural health monitoring (SHM) is critical for safety and reliability in aerospace, civil infrastructure, and renewable energy systems. Fibre Bragg Grating (FBG) sensors are widely used for real-time shape tracking but face limitations in cost-effective, high-resolution defect detection and predictive modelling.
This project, in collaboration with Airbus, Insensys, and Target3D, will:
Develop AI-enhanced polarimetric DOFC sensing for structural defect monitoring in composite laminates.
Improve localisation and classification of defects in materials.
Predict defect evolution under operational conditions.
Deliver cost-effective, industrially adaptable solutions for shape sensing.
The candidate will gain experience in photonic sensing, experimental optics, fibre sensing technologies, and machine learning applied to structural monitoring.
Entry Requirements
Bachelor’s degree (First Class or 2:1) in a relevant subject, or Bachelor’s plus Master’s (Merit or higher).
Strong foundation in physical optics and photonics.
Desirable: knowledge of scientific programming, numerical modelling, mathematical physics, differential equations, experimental optics, or fibre sensing.
Strong foundation in physical optics and photonics.
Desirable: knowledge of scientific programming, numerical modelling, mathematical physics, differential equations, experimental optics, or fibre sensing.
How to Apply
Submit complete application including: transcripts/certificates, research statement, personal statement, CV, two academic references, English language evidence, and passport copy.
Contact email: pgr_admissions@aston.ac.uk
Contact email: pgr_admissions@aston.ac.uk
Eligibility
UK/Home
EU
International
Supervisor Profile
DS
Dr S Sergeyev
Aston University, College of Engineering and Physical Sciences
Related Opportunities
KEN
ICL
UOC
LU