Master Thesis "Material Aware Inline 3D Imaging"

AIT Austrian Institute of Technology; Wien; Vor Ort; EUR 10.433 - 12.497; Teilzeit

Zusammenfassung

The AIT Austrian Institute of Technology in Vienna invites applications for a master's thesis position focusing on 3D reconstruction methods for industrial inspection. The project involves evaluation of material-aware techniques in collaboration with TU Wien. Ideal candidates are master's students in related fields with strong knowledge of computer vision and programming skills in Python or C++. The position offers EUR 1,027.40 monthly for 20 hours/week with additional benefits, and spans over 6 months, starting ideally in June 2026.

Qualifikationen

• Ongoing master’s studies at TU Wien.
• Good knowledge in computer vision and/or image processing required.
• Interest in machine vision, computer graphics and machine learning.
• Hands-on lab work experience is advantageous.
• Very good English skills (spoken and written) required.

Aufgaben

• Acquire basic skills in inline optical 3D imaging.
• Conduct a literature search and review regarding optical detection.
• Explore material-aware Gaussian Splatting and related approaches.
• Access machine vision lab for experiments and dataset collection.

Kenntnisse

Computer vision

Image processing

Python

C++/CUDA

OpenCV

Blender

Machine learning

Ausbildung

Master’s studies in Geodesy and Geoinformation

Computer Vision/Image Processing

Computer Science

Physics

Mathematics

Jobbeschreibung

Overview

As Austria's largest research and technology organisation for applied research, we are dedicated to making substantial contributions to solving the major challenges of our time, climate change and digitalisation. Our Center for Vision, Automation & Control located in Vienna invites applications for a master’s thesis position. The Center exploits automation and digitalisation to initiate and advance innovation for industry, primarily in Austria and Europe.

For this master’s thesis project, you will join the Competence Unit “High-Performance Vision Systems (HVS)”, which has over 20 years of research experience in industrial inspection and quality assurance. At HVS we innovate 2D & 3D vision systems that go beyond the state of the art by developing advanced vision sensor concepts, scalable embedded vision systems, and machine learning methods for industrial high‑performance visual quality inspection.

The objective of this master’s thesis project is to evaluate the potential of material aware 3D reconstruction methods for industrial fast inline inspection applications. While many state‑of‑the‑art optical inline 3D scanners, such as laser triangulation sensors, primarily deliver geometric depth information, industrial processes often require deeper object knowledge beyond geometrical dimensions. Color, roughness, reflectance behaviour, and many other optically detectable material properties carry essential information for robust defect detection and quality control.

Cooperation

Within our HVS unit we established a top‑notch technology for inline computational imaging of color and photometric information alongside 3D depth information. Building on this, the master’s thesis, which will be carried out in cooperation with TU Wien – Research Group Photogrammetry, will combine application‑oriented research with academic expertise in photogrammetry, remote sensing, and advanced 3D reconstruction methods. The specific thesis’ focus will be adapted depending on the student’s skill and interests, e.g. towards the integration of material‑aware Gaussian splatting representation or multispectral imaging.

• You will acquire basic skills in inline optical 3D imaging for process control and automation.
• You will carry out a literature search and review on state‑of‑the‑art optical detection and reconstruction of material properties.
• You will explore the potential of material‑aware Gaussian Splatting and related approaches or sensing methods like multispectral imaging for fast inline quality control.
• Under the guidance of our experienced researchers, you will have access to our machine vision lab as well as a digital‑twin Blender model of the lab setup, to carry out experiments and collect datasets.
• Depending on the direction of the thesis, there is the possibility to expand the existing lab setup with multispectral sensing capabilities.
• A selected Gaussian Splatting algorithm (e.g. RefGS, 3DGSDR, or GShader) can serve as a starting point for further adaptation towards modelling and reconstructing material properties and can be extended or complemented depending on your individual research direction.

Qualifications

• Ongoing master’s studies at TU Wien in the field of Geodesy and Geoinformation, Computer Vision/Image Processing, Computer Science, Physics, Mathematics or similar.
• Good knowledge in computer vision and/or image processing is required.
• Interest in machine vision, computer graphics and machine learning.
• Experience in at least one of the programming languages Python, C++/CUDA, OpenCV or Blender experience is advantageous.
• Hands‑on lab work experience with image acquisition using industrial/digital imaging is advantageous.
• Experience in material‑aware object representation such as Ref‑Gaussian or Material‑Informed GS is advantageous.
• High level of commitment and team spirit.
• Very good English skills (spoken and written).

What To Expect

• Duration of the master’s thesis project: 6 months.
• Start date: ideally June 2026 – with some flexibility depending on your availability.
• EUR 1,027.40 gross per month for 20 hours/week based on the collective agreement. There will be additional company benefits.
• Opportunity to work on a real‑world research challenge that arose in ongoing collaborations with industrial partners and gain hands‑on experience with cutting‑edge imaging technologies at the forefront of application‑driven research.
• Mentoring and a well‑established research setting with joint supervision from AIT and TU Wien.

At AIT, we create an inclusive and family‑friendly working environment that promotes equal opportunities and actively strengthens diversity across our workforce and in leadership positions. Among other initiatives, we are committed to increasing the proportion of women in our company and therefore particularly welcome applications from female applicants.

Please submit your application documents including your CV, cover letter, relevant certificates (transcript of records) as well as a proof of identity online.

Apply now!