ABOUT THE ROLE
Working on a UK–Brazil project, you will advance next‑generation optical (flash & high‑power LED) and inductive thermography to assure the reliability of large composite wind‑turbine blades.
In this role you will:
1. design, build and optimise optical and inductive thermal NDE rigs for curved, metre‑scale blade sections;
2. develop and validate forward–inverse heat‑transfer models to reconstruct subsurface defects;
3. implement image/signal‑processing or machine‑learning pipelines for automated flaw characterisation;
4. collaborate with the Federal University of Rio de Janeiro, including short research visits;
5. disseminate findings via high‑impact journals, international conferences and stakeholder workshops;
6. mentor postgraduate researchers and contribute to day‑to‑day lab management.
This is a full-time, fixed-term position for a duration of two years. The role is based within the Department of Computer and Information Sciences at Northumbria University, located in Newcastle upon Tyne.
ABOUT THE TEAM
You will join the Thermal NDE Research Team within the Department of Computer and Information Sciences. The group hosts state‑of‑the‑art IR cameras, induction coils and GPU‑accelerated computing clusters.The project team spans Northumbria University and the Federal University of Rio de Janeiro. You will work alongside academics, post‑docs, PhD students and industrial engineers in a vibrant, collaborative environment at our Newcastle city‑centre campus.
ABOUT YOU
To succeed, you will bring:
7. PhD (or thesis submitted) in Mechanical/Materials/Electrical Engineering, Physics or related field;
8. demonstrable expertise in thermal and/or electromagnetic NDE (optical, inductive thermography or electromagnetic sensing) or heat‑transfer modelling of composite structures;
9. hands‑on experience with IR cameras and active‑heating rigs;
10. strong programming skills (Python/Matlab and/or FEM packages);
11. publication record appropriate to career stage;
12. excellent teamwork, project‑management and communication skills.
Desirable extras include experience with wind‑energy structures, machine‑learning/inverse‑problem techniques and prior industrial collaboration.
Further details about the role requirements are available in the job description.