Doctoral Researcher in Engineering Science
PhD position combining non-destructive testing of structural steel with machine learning at the University of Luxembourg.
Best for:Best for engineering graduates with hands-on laboratory experience and an interest in applying machine learning to structural materials assessment.
Overview
This doctoral research position at the University of Luxembourg focuses on the non-destructive assessment of structural steel intended for reuse, a topic central to sustainable construction and circular-economy goals. The project involves intensive laboratory work—accounting for more than half of the researcher's time—alongside the application of machine-learning techniques to analyse and interpret testing data. The position is based at the Belval campus in Esch-sur-Alzette and is offered as a 36-month, full-time appointment with an annual salary of approximately €43,445. Candidates should be prepared for a hands-on, interdisciplinary research environment bridging structural engineering, materials science, and data-driven methods.
What you'll do
- Conduct non-destructive assessment of structural steel components intended for reuse in construction
- Perform intensive laboratory experiments, comprising more than 50% of the working time
- Develop and apply machine-learning models to analyse and interpret testing data
- Design experimental protocols for evaluating material properties without damaging specimens
- Document research findings and prepare results for publication in peer-reviewed venues
- Collaborate with interdisciplinary teams across engineering and data-science domains
- Present progress and results at internal meetings and external conferences
Requirements
Degree required: MSc in Structural Engineering, Materials Science, Civil Engineering, or a closely related field
Degree offered: PhD
Language: English
Instruction: English
- Strong background in structural engineering or materials science
- Hands-on laboratory experience with materials testing
- Knowledge of non-destructive testing methods
- Programming skills for data analysis and machine learning
- Ability to work independently in a laboratory setting
- Must hold a Master's degree or equivalent in a relevant engineering or science discipline
- Available for full-time, on-site work at the Belval campus in Esch-sur-Alzette, Luxembourg
Funding & terms
How to apply
Interested candidates should apply through the University of Luxembourg's official application channels. The application requires submission of academic credentials and supporting documents as specified in the full job announcement.
- CV
- Cover letter
- Academic transcripts (MSc and BSc)
- Contact information for references
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