EngD position: Data-Driven Reliability Analysis of Underground Infrastructure Maps (ZoARG EngD)
In this EngD project, you will develop an innovative data-driven model to assess the reliability of underground infrastructure maps, working alongside experts from the University of Twente and leading excavation chain partners to analyse field data, build a prediction model, and contribute directly to a safer and more professional infrastructure sector.
Background
The Netherlands operates a digital system for exchanging location data of infrastructure networks, such as underground cables and pipelines. This system (known as KLIC) is managed by Kadaster, while network owners are responsible for providing accurate data about the location of their buried assets.
Many utility maps used in construction projects combine outdated and recently surveyed network drawings. As a result, it is often unclear whether a map is up-to-date, complete, or accurate. To verify this, project teams dig trial trenches during construction and maintenance works to compare the actual underground situation with the map.
The Challenge
Contractors and municipalities hold hundreds of reports containing sketches and analyses of trial trenches. These often include comparisons between the actual cable positions and the official network maps (KLIC deliveries). A analysis of these datasets yield valuable insights into the conditions under which maps are more or less reliable. However, such an analysis has not yet been conducted. This leaves stakeholders unable to statistically and systematically assess the quality of official network maps.
Could this be done differently? Is it possible to systematically analyse all trench documentation using data-driven methods to better interpret the reliability of underground maps?
Your Role
As an EngD candidate, you will work under supervision to develop a data-driven model for assessing the reliability of official network maps (KLIC deliveries). You will build a prototype algorithm that can be used by infrastructure and civil engineering professionals to better evaluate cable and pipeline location data.
Your tasks will include:
1. Analysing existing trench datasets (content, format, resolution, completeness)
2. Defining the concept of ‘reliability’
3. Exploring suitable data mining and analysis methods
4. Collecting case data
5. Selecting predictive parameters
6. Training algorithms (e.g. decision trees, gradient boosting, neural networks)
7. Validating the prototype with end users
Your Team: ZoARG
This project is part of the ZoARG2.0 programme, in which seven major excavation chain partners (Alliander, Enexis, Gasunie, Heijmans, Kadaster, KPN, Siers, and Vitens) collaborate on research, development, and education in excavation damage prevention and responsible digging. Three of these organisations form the project steering committee.
Between 2025 and 2028, five EngD projects will be launched within ZoARG2.0. Candidates will work with UT researchers to advance scientific knowledge (digital technologies, process models, and training) aimed at improving safety in the excavation sector. ZoARG projects are carried out in close collaboration with industry partners, allowing candidates to develop both academically and professionally.
**Start date: March 2026
Duration: 2 years**
Information and application
Submit your application by 21 November 2025.
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
Selected candidates will be invited for an (online) interview with the academic supervisors. Interviews will take place between 15 and 30 December 2025, with invitations sent from 1 December.
Preferred candidates will proceed to a matching interview with the project steering committee.
Screening is part of the procedure.
About the organisation
The Faculty of Engineering Technology (ET) engages in education and research of Mechanical Engineering, Civil Engineering and Industrial Design Engineering. We enable society and industry to innovate and create value using efficient, solid and sustainable technology. We are part of a ‘people-first' university of technology, taking our place as an internationally leading center for smart production, processes and devices in five domains: Health Technology, Maintenance, Smart Regions, Smart Industry and Sustainable Resources. Our faculty is home to about 2,900 Bachelor's and Master's students, 550 employees and 150 PhD candidates. Our educational and research programmes are closely connected with UT research institutes Mesa+ Institute, TechMed Center and Digital Society Institute.
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