PhD in Structural Modelling and Health Monitoring of Composite and Complex Structures and Systems

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Organisation
KU Leuven
Study Options
Full Time

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 40 best worldwide. For three years in a row, Thomson Reuters ranked KU Leuven as Europe's 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 fully funded, full-time PhD post within the Department of Mechanical Engineering of the University. The successful candidate will be affiliated and work within the renowned Noise and Vibration Research Group of KU Leuven. She/he will be based at the Ghent Technology Campus with regular visits and full access to equipment at the main campus in Leuven.

The successful candidate will be expected to make significant contributions to the development of robust damage identification 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: dimitrios.chronopoulos@kuleuven.be

Keywords: Structural Health Monitoring, Ultrasound and Vibration, Composites, 3D Printed Structures, Damage, Identification, Structural Health Prognosis, Bayesian Statistics, Machine Learning


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