At the DLR Institute of Transportation Systems we conduct research into technologies for the intermodal connected and automated transport of the future on road and rail. To achieve this, we are working in interdisciplinary teams with a total of 250 scientists on the development of innovative operating concepts and methods. Our goal is to ensure climate-neutral and sustainable mobility in cities and regions.
What to expect
During the development of solutions for the intermodal traffic system, our Institute takes into account the requirements of both the users and the operators. Through the analysis of the required information flows, we support the planning and operation of intermodal overall systems and provide the basis for their technical and operational validation and verification. These systems make it possible to strengthen local public transport and make cities and regions more attractive. For transport hubs such as airports, railway stations or bus stops, we research and optimise infrastructures, processes and the interlinking of all modes of transport. This enables us to increase the safety and predictability of the transport of people and goods.
Your tasks
- Analysis and preprocessing of geospatial data (e.g., OSM, orthophotos, accident data)
- Contributing to the development of AI / machine learning approaches for identifying infrastructural “pain points” affecting cyclists
- Implementation and evaluation of models in Python
- Working with QGIS for data exploration, visualization, and validation
- Extraction and analysis of features from geospatial and image data
- (Optional) Contribution to scientific publications or a thesis within the project
Your profile
- Currently enrolled in a degree program such as Geoinformatics, Data Science, Computer Science, Geography, Transportation Engineering, Civil Engineering, or a related field
- Interest in traffic safety, geospatial data (e.g., OSM), cycling infrastructure, and data-driven infrastructure analysis
- Good programming skills in Python
- Ideally first experience with GIS tools (e.g., QGIS)
- Interest or initial experience in machine learning / deep learning
- Ideally first experience with computer vision
- Independent, analytical, and structured working style, as well as curiosity and strong intrinsic motivation to engage with complex research questions
We look forward to getting to know you!
If you have any questions about this position (Vacancy-ID 4506) please contact:
Dr. Sascha Knake-Langhorst
Tel.: +49 531 295 3474
