EngD position: AI for Underground Infrastructure Detection and Characterisation

University of Twente (UT); Netherlands; On-site; EUR 32,000 - 39,000; Full time

Benefits offered by this job

Pension & health care

Tailor-made programme

Holiday allowance

Year-end bonus

Campus facilities

Job summary

University of Twente (UT) invites applications for a two-year EngD position to develop an AI model that detects underground infrastructure in GPR radargrams and estimates depth. You will work within ZoARG|ReDUCE, collaborating with CEM, AES, UT FieldLab, and industry partners.

This EngD combines research and education, with a tailor-made programme and full UT employment including pension and health care. The gross monthly salary is €3173, plus 8% holiday allowance and 8.3% year-end bonus, and at

Qualifications

• Master’s degree or equivalent in Civil Engineering, Geomatics, Computer Science, Data Science, or related field.
• Experience with machine learning, data analytics, or computer vision techniques.
• Programming in Python and familiarity with ML frameworks such as PyTorch, TensorFlow, or similar.
• Excellent English communication skills.

Responsibilities

• Analyze GPR interpretation methods, ML techniques, and relevant software tools.
• Explore AI approaches for automated utility characterization.
• Prepare and manage large GPR datasets from the Utility Mapping Site (UMS).
• Design, train, and validate ML models for interpreting GPR radargrams.
• Compare developed models with literature and commercial software.

Skills

Machine learning

Data analytics

Python

English communication

Team collaboration

Education

Master’s degree or equivalent in Civil Eng/Geomatics/CS/Data Science

Tools

PyTorch

TensorFlow

Job description

Organisation/Company University of Twente (UT) Research Field Engineering » Civil engineering Engineering » Computer engineering Researcher Profile Recognised Researcher (R2) Application Deadline 27 Nov 2026 - 22:59 (UTC) Country Netherlands Type of Contract Temporary Job Status Not Applicable Hours Per Week 38.0 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

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
• 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

Additional Information

• Two-year full-time EngD position, where scientific and research domains are combined optimally with education and practical implementation of innovative design
• Full status as an employee at the UT, including pension and health care benefits
• A tailor-made post-master build programme that has an educational component (~40%) as well as a design project (~60%)
• A gross monthly salary of €3173
• An annual holiday allowance of 8% of the gross annual salary, and an annual year-end bonus of 8.3%
• Minimum of 29 holidays per year in case of full-time employment
• A work environment on a green and lively campus with (free access) to sports and leisure facilities
• Mentorship that supports your professional and personal growth
• Upon finishing the programme, you will be awarded a certified degree. You can use the academic title EngD and register as a Technological Designer in the Dutch registry of the Royal Institution of Engineers in the Netherlands (KIVI).

Additional comments

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: [e-mail] or [e-mail].

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.