Marie-Curie (MSCA) doctoral fellowship: PhD in Structural Dynamics and Health Monitoring of Composite and 3D printed Structures and Systems KU Leuven in Belgium

KU Leuven consistently ranks among the top 50 universities in the world by major ranking tables with the Department of Mechanical Engineering being ranked amongst the 30 best worldwide. For four years in a row, Thomson Reuters ranked KU Leuven as Europe's no.1 most innovative university, with its researchers having patented more products than any other university in Europe.

Project description: Structural failure in the aerospace, civil and energy sectors 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 and characterization of minor failures within complex (composite as well as 3D printed) structures can lead to a radical reduction of this cost.

Applications are invited for a Marie-Curie doctoral fellowship: https://marie-sklodowska-curie-actions.ec.europa.eu/actions/doctoral-networks This is a full-time PhD post within the Department of Mechanical Engineering of the KU Leuven. The successful candidate will be affiliated and work within the renowned Noise and Vibration Research Group of KU Leuven.

The successful candidate will be expected to make significant contributions to the development of robust damage assessment tools for complex industrial structures. Applicants should be able to demonstrate consistent knowledge in the areas of engineering dynamics and/or numerical modelling and/or data post-processing.

This funding call is open to all EU and International applicants. Informal enquiries prior to making an application should be addressed along with a detailed CV to Prof. D. Chronopoulos: [email protected]

Keywords: Ultrasound and Vibration, Structural Dynamics, Composite Structures, 3D Printed Structures, Damage, Assessment, Structural Health Monitoring, Structural Health Prognosis, Bayesian Statistics, Machine Learning