About this position
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
Requirements
Specific Requirements
- A Master’s degree or equivalent experience in Civil Engineering, Geomatics, Computer Science, Data Science, or a related field
- Experience with machine learning, data analytics, or computer vision techniques
- Programming experience in Python and familiarity with machine learning frameworks such as PyTorch, TensorFlow, or similar tools
- Curiosity about geospatial data, remote sensing, subsurface sensing, or utility mapping applications;
- Strong analytical and problem-solving skills
- The ability to work independently and collaborate effectively with academic and industrial partners
- Excellent communication skills and proficiency in English
How to apply
- Read the full advert on the source site — it carries the authoritative terms.
- Prepare your SOP, CV, transcripts and referees before the deadline.
- Apply through the university's own portal. Never pay a fee to a third party.