Master Thesis: Automated alignment of telecom site DTs and city-scale maps

Technology, Data & Digital · Software & Web Development · Software Engineering · Data Science

In short

Master Thesis project focused on developing automated methods to align detailed telecom Site Digital Twins (from UAV/LiDAR, NeRF, BIM) with city-scale 2.5D/3D maps (USDC, glTF, Mitsuba XML). The role involves implementing and testing alignment pipelines using computer vision, 3D geometry, and machine learning techniques, measuring results, and extending existing Ericsson solutions.

Responsibilities

Develop and evaluate automated methods to align detailed telecom Site Digital Twins with city-scale 2.5D/3D maps.
Work across different representations, such as UAV- or LiDAR-derived point clouds, NeRF models and BIM, and city-scale maps in formats such as USDC, glTF and Mitsuba XML.
Investigate geometric and spatial discrepancies between the detailed site models and the broader map context, and develop approaches to improve alignment and consistency.
Implement and test alignment or registration pipelines, using suitable computer-vision, 3D-geometry and machine-learning techniques.
Measure the quality and robustness of your approach, analyse failure cases and document findings relevant to radio-propagation modelling and Network Digital Twins.
Extend existing Ericsson solutions and present your results and recommendations to the research team.

Requirements

Pursuing a Master’s degree (or equivalent) in computer science, electrical engineering, robotics, computer vision, geomatics or a related field, and are looking for a thesis project.
Comfortable programming in Python and at least one additional language, such as C++, JavaScript or another relevant language.
Ability to reason about 3D geometry and spatial data, evaluate technical results and communicate your approach clearly.
Thoughtful use of modern AI-assisted research and programming tools, with the ability to independently understand, debug, validate and take ownership of your code.

Desired Qualifications

Experience with point-cloud registration, coordinate systems, UAV/LiDAR data, city-map formats or digital twins is an advantage.
Familiarity with at least one relevant area, such as 3D computer vision, point clouds, 3D graphics or modelling, rendering, photogrammetry, BIM or NeRFs is an advantage.

Benefits

Outstanding opportunity to use your skills and imagination to push the boundaries of what’s possible.
Chance to build solutions never seen before to some of the world’s toughest problems.
Be challenged, but not alone; join a team of diverse innovators.
Opportunity to go beyond the status quo to craft what comes next.

Skills

PythonC++JavaScriptpoint-cloud registrationcoordinate systemsUAV/LiDAR datacity-map formatsdigital twins3D computer visionpoint clouds3D graphicsmodellingrenderingphotogrammetryBIMNeRF

#thesis#master thesis#automated alignment#telecom#digital twins#city-scale maps#UAV#LiDAR#pointclouds#NeRF#BIM#USDC#glTF#Mitsuba XML#computer vision#3D geometry#machine learning#radio propagation modelling#network digital twins

About Ericsson

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