EngD position: AI for Underground Infrastructure Detection and Characterisation
Develop data-driven processing methods to further automate detection and characterization of cables and pipelines in Ground Penetrating Radar data. The future of utility mapping.
Reliable information about underground infrastructure is essential for safe excavation, asset management, and the digital transformation of the subsurface sector. While Ground Penetrating Radar (GPR) is increasingly used for utility detection, interpreting radar data remains a complex and time-consuming task that requires specialist expertise. Recent advances in Artificial Intelligence (AI) offer new opportunities to automate this process and improve the efficiency and reliability of utility mapping.
In this EngD project, you will develop an AI model that automatically detects underground infrastructure in GPR radargrams and estimates its depth. The project builds on the growing availability of high-quality GPR data collected at the University of Twente’s Utility Mapping Site (UMS), a unique test environment for utility mapping technologies.
Current machine learning models and their training data are limited in size, comprehensiveness, and realism – resulting in partial automation with limited performance. This constrains their usefulness in real-world conditions. Your challenge is to develop and validate machine learning models using systematically collected and accurately annotated GPR datasets. By combining geospatial data, subsurface sensing, and AI, you will contribute to the next generation of utility mapping technologies and support safer excavation practices.
Your environment
This project is part of the ZoARG|ReDUCE programme, a collaborative initiative aimed at minimizing excavation damage to underground infrastructure in the Netherlands. You will work within a multidisciplinary environment that includes:
- The University of Twente’s Departments of Civil Engineering and Management (CEM) and Applied Earth Sciences (AES)
- The Utility Mapping Site (UMS) at the UT FieldLab
- Industry collaborators involved in the ZoARG programme
What you will do
- Analyse existing GPR interpretation methods, machine learning techniques, and relevant software tools
- Explore and evaluate AI approaches for automated utility characterization
- Prepare, preprocess, and manage large GPR datasets collected at the Utility Mapping Site
- Design, develop, train, and validate machine learning models for interpreting GPR radargrams
- Compare developed models with existing approaches reported in literature and commercial software solutions
- Work in close partnership with infrastructure owners, contractors, technology providers, and researchers engaged in the ZoARG programme
- Report findings and translate results into practical recommendations for measurement practice and technology evaluation
Information and application
Submit your application by 27 November 2026. Your application must include:
• A recent CV detailing relevant academic and (if applicable) professional experience
• A motivation letter (max. 1.5 pages) explaining your interest and relevant background for this project
• An overview of your MSc degree: including thesis title, abstract, and grade list
For questions about the project or your eligibility, please contact the selection committee at: l.l.oldescholtenhuis@utwente.nl or r.b.a.terhuurne@utwente.nl.
Selected candidates will be invited for an (online) interview with the academic supervisors. Interviews will take place on 11 and 18 December.
Starting date of this position is in the first half of 2027.
Preferred candidates will proceed to a matching interview with the project steering committee.
Screening is part of the procedure.
About the organisation
At the Faculty of Engineering Technology (ET), we work on engineering for impact: developing smart, sustainable, human-centred and technological solutions for societal challenges. We connect fundamental education, research and practice across five core domains: Asset & Maintenance engineering, Intelligent Manufacturing Systems, Personalised Health Technology, Resilience Engineering, and Sustainable Production, Energy and Resources.
We work on education and research in mechanical engineering, civil engineering and industrial design engineering. Together, we learn by making, creating, and innovating, addressing challenges in a solution-oriented way. Quality, connection and inclusivity are the foundation of our culture.
In our open community, students, researchers and staff collaborate with industrial and societal partners. This enables us to develop insights, applications and solutions that add value to society.
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