Imdea

OFFER:

PhD candidate – AI-Driven Optimization of Ultrasonic Inspection in Composite Materials with Validation via Computed Tomography

SHORT DESCRIPTION

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.

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OTHER DETAILS

Ref. num. 2024-FS-R1-194

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Si ha aceptado que su CV pueda ser consultado en otros procesos de selección, sus datos serán conservados para participar en procesos distintos al de la vacante a la que se ha inscrito.