Jobid: 637868401911922981 (0.0509)
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
Your profile
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
Our offer
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).
Lees hier meer…
Deel deze vacature:
