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# Computer scientist

- **Company:** Remote Sensing Technology Institute
- **Location:** Austria
- **Contract:** Permanent contract
- **Skills:** Geographic Information Systems, Artificial Intelligence, ArcGIS (Software), Computer Vision, Computer Programming, Python (Programming Language), Machine Learning, Quantum GIS (QGIS), Pytorch, Deep Learning, Information Technology
- **Published:** August 28, 2026
- **Apply:** https://itjobsaustria.at/u/jobs/submit-application/12417/Computer-scientist-for-traffic-area-segmentation-f-m-d?contact_mail=contact-dlr%40dlr.de&job_title=Computer+scientist+for+traffic+area+segmentation+(f%2Fm%2Fd)

## About the Role

* Completed academic university degree (Diploma/Master's) in computer science, machine learning, or a comparable field of study
 * Advanced programming skills in Python and experience with deep learning frameworks (especially PyTorch)
 * Practical experience with state-of-the-art computer vision and deep learning models such as CNNs and transformers
 * Experience in applying AI methods and optimizing performance with respect to accuracy and processing speed
 * Ability to work collaboratively in an interdisciplinary team, strong problem-solving, communication, and presentation skills
 * Good English skills (B2 level or higher)
 * Experience working with remote sensing data and GIS software (e.g., ArcGIS, QGIS) is an advantage
We look forward to getting to know you!

## Description

Remote sensing, with its various sensors and platforms, is a valuable data source for traffic research. Entire cities and regions can be captured on a large scale and analyzed with respect to traffic-related questions.
The Institute for Remote Sensing Methodology regularly acquires aerial imagery using the aircraft and helicopters of the DLR research fleet, as well as institute-owned camera systems. In addition, the institute has access to high-resolution satellite imagery. To make optimal use of these sensor systems, methods and algorithms are developed for the automatic extraction of traffic objects and traffic areas.
These innovative algorithms play a role, for example, in the development of highly accurate, de-tailed maps for automated driving or in improving micro- and macroscopic traffic models. Novel deep learning algorithms achieve very promising results, which can be further improved and adapted to the respective task.

your tasks

 * Further development of deep learning algorithms (AI methods) for application on high-resolution aerial and satellite data to capture traffic areas, including their functions (e.g., roads, access routes, bicycle paths, etc.)
 * Development of a pre-operational software processor, including AI algorithms, for large-scale mapping of traffic areas
 * Validation of results using independent datasets and accuracy assessments of the developed methods
 * Collaboration with project partners to utilize the data for addressing traffic science-related questions
 * Scientific publication of results and presentation at national and international conferences


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