PhD in Health Monitoring of Aircraft Structures and Systems (ref. BAP-2025-697)

Applications are invited for a PhD fellowship. This PhD is situated in a joint project between the Departments of Mechanical and Materials Engineering of the KU Leuven - Campus Ghent, to fully cover materials as well as operational aspects. The successful candidate will be affiliated with the renowned “Mecha(tro)nic System Dynamics” and “Materials Performance and Nondestructive Testing” Divisions of the KU Leuven.

https://www.mech.kuleuven.be/e...

Project

Project description: Structural failure in the aerospace sector can be life threatening for users/passengers and financially catastrophic for operators and the manufacturer. This is the main reason for which a large part of an aircraft’s lifecycle operation cost is spent for inspecting its structural integrity on the ground. On-line detection, autonomous and characterization of failures within aircraft structures can lead to a radical reduction of this cost while increasing safety.
As a project aiming at industrial implementation, there is a distinguished industrial advisory group in place, whose members, such as Brussels Airlines support the work and can be a potential partners for further valorisation.

Profile

The successful candidate will be expected to make significant contributions to the development of robust monitoring and damage assessment tools for complex industrial structures. Applicants having an appropriate background in natural or engineering sciences should be able to demonstrate consistent knowledge in areas such as structural monitoring and/or engineering mechanics and/or numerical modelling and/or data post-processing. The research activities will be performed within the research groups active at the KU Leuven Ghent Campus.

Offer

This funding call is open to all EU and International applicants.
Keywords: Digital Image Correlation, Experimental and Computational Mechanics, Ultrasound and Vibration, Aircraft Structures, Damage Assessment, Structural Health Monitoring, Structural Health Prognosis, Bayesian Statistics, Machine Learning