.The candidate will participate in a public funded research project (CompoSTLar – "Boosting the digital transformation of aviation supply chains for advanced composite aerostructures, an Horizon CL5-2024-D5-01-08 project" ) aiming at developing a holistic, AI-powered, and digitally integrated ecosystem for the advanced design, manufacturing, maintenance, and recycling of novel graphene-functionalized thermoplastic composite aerostructures, with a focus on zero-defect production, intelligent repair, and sustainable circular manufacturing.This PhD focuses on AI model implementation for discovery of defects in composite material (CFRP) from ultrasonic measurements, by mean of automated data annotation from X-ray computed tomography data. The ultrasonic (US) data will be provided by project partners from US in-situ monitoring of the CFRP during automated tape layering.The candidate will test and optimize new machine learning models including (but not restricted to) k-neighbours, support vector machines, ensemblings, and neural networks.OTHER DETAILSRef. num. 2024-FS-R1-194PhD candidate – AI-Driven Optimization of Ultrasonic Inspection in Composite Materials with Validation via Computed TomographyIMDEA Materials Institute is a public research organization founded in 2007 by Madrid's regional government to carry out research of excellence in Material Science and Engineering by attracting talent from all over the world to work in an international and multidisciplinary environment. IMDEA Materials has grown rapidly since its foundation and currently includes more than 120 researchers from 22 nationalities and has become one of the leading research centers in materials in Europe which has received the María de Maeztu seal of excellence from the Spanish government. The research activities have been focused on the areas of materials for transport, energy, and health care and the Institute has state-of-the-art facilities for processing, characterization and simulation of advanced materials.DescriptionThe presence of defects in carbon fibre reinforced composite materials is a common manufacturing defect that could endanger the in-service performance of components. To ensure quality standards, the industry relies on ultrasonic non-destructive testing due to its cost and ease of use. However, to date, ultrasonic methods have not been able to assess porosity levels independently of other attributes such as pores morphology, size, and distribution, or even other types of defects. One possible solution is to address the problem of different defect types with ultrasonic propagation utilizing data-driven methodologies. The use of machine learning models could discover the hidden patterns of the interaction between the defects and the ultrasound wave. A possible solution relates features of the ultrasound wave with porosity characteristics obtained from three-dimensional (3D) reconstructed XCT volumes of carbon fiber composites via machine learning models