Robust industrial defect identification, classification and quantification using low resolution non-destructive inspection techniques for automated digital shadowing

DESCRIPTION

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.

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.
• Participate in doctoral training activities, consortium meetings, and international secondments at partner organisations.

REQUIREMENTS

• Master’s degree (or equivalent qualification giving access to doctoral studies) in Engineering (Industrial, Aerospace, Materials Engineering), Physics, or a related field, obtained before the recruitment date.
• Eligibility for admission to the Doctoral Programme in Technology at the University of Girona, including the academic requirements for doctoral studies (normally at least 300 ECTS in total, including 60 ECTS at Master's level).
• Must not already hold a doctoral degree (PhD).
• Compliance with the MSCA mobility rule: applicants must not have resided, worked, or studied in Spain for more than 12 months during the 36 months immediately prior to recruitment.
• Sufficient written and spoken English proficiency to conduct research, participate in international training activities and secondments, and communicate scientific results.
• At the time of incorporation, candidates must provide proof of enrolment or admission to a doctoral programme for the 2026/2027 academic year.

Preferred qualifications

• Documented knowledge, coursework, or thesis experience in ultrasonics, composite manufacturing, or non-destructive testing (NDT/NDI).
• Strong interest and skills in ultrasonics, composite manufacturing, and non-destructive testing methods.

Specific experience prioritised for DC2

• Documented knowledge (through education, courses, or thesis projects) in ultrasonics, composite manufacturing, or non-destructive testing.