The Doctoral Candidate will be expected to develop Machine Learning tools that enable automated, objective and efficient identification, classification and quantification (ICQ) and spatial mapping of meso- and macro-scale defects in composite materials, through training on coupled high-resolution and low-resolution non‑destructive inspection data (XRM and industrial ultrasonic imaging data). The successful candidate will develop scientific concepts and communicate research results through scientific publications and presentations at international conferences. The candidate will collaborate closely with fellow doctoral candidates within the LEGEND network, exploiting synergies across projects, and will actively participate in General Assembly meetings, training events, and international secondments across Europe. The research will focus on improving industrial non‑destructive inspection techniques, developing deep‑learning based tools for automated defect characterisation, and contributing to the generation of accurate digital shadows for composite structures.




Si los siguientes requisitos del puesto y la experiencia coinciden con sus habilidades, por favor, asegúrese de enviar su solicitud sin demora.
Key Responsibilities
- Develop ML and deep‑learning based tools for automated identification, classification and quantification of defects from low‑resolution non‑destructive inspection data.
- Train and validate models using coupled high‑resolution (XRM) and industrial ultrasonic imaging datasets.
- Assess and improve the robustness of industrial low‑resolution NDI techniques for composite structures.
- Evaluate the accuracy and scalability of automated defect characterisation methods for aerospace composite components.
- Publish research findings in scientific journals and present results at international conferences.
- Collaborate with researchers, industrial partners, and fellow doctoral candidates within the LEGEND consortium. xqbhyrx
- Participate in doctoral training activities, consortium meetings, and international secondments at partner organisations.
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📌 Robust industrial defect identification, classification and quantification using low resolution[...] (España)
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