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EngD position: AI for Underground Infrastructure Detection and Characterisation

EURAXESS hosting organisation · عدة بلدان أوروبية

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

At a glance
Where
Multiple European countries
Deadline
2026-09-30
Funding
EUR 3173 (parsed from page — verify)
Board
EURAXESS

Who can apply

Criteria stated by the funder on the official source.

Nationality
EURAXESS vacancies rarely print a nationality table. Confirm whether applicants from Tunisia can apply and whether a residence/work permit pathway exists on the employer page.
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Education
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-s
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Funding and what it does not cover

Funding details were taken from the official programme page text. Confirm the current call before applying.