At the Institute of Data Science in Jena, we are working on making the data backbone a reality for all DLR application areas (aviation, space, energy, transportation, security). To this end, we develop and research methods in interdisciplinary work with a focus on applications such as sustainable and circular processes, resilient supply chains, data-driven value chains or robust decision support. The methods developed in this way are applied in cooperation with other DLR institutes and external partners, either as part of joint projects or as part of technology transfer activities.
The research and development work of the Data Acquisition and Mobilisation department aims to provide high-quality data for industry and science. This forms the basis for achieving sustainable interdisciplinary value chains and for data-driven decision support in complex systems. The key challenges here are usability, availability and access.
What to expect
The current MoDa (Models and Data for Future Mobility) project is developing innovative mobility solutions to support the mobility transition and creating the framework for a comprehensive data, modelling and service ecosystem. The Power Forecast Mapper sub-project pools the expertise of numerous DLR institutes in order to model the future charging requirements of electromobility at a fine scale and derive suitable locations for the expansion of the charging infrastructure. Comprehensive geodatabases are currently being set up as a data and validation basis for the transport and charging models.
Your tasks
Using web scraping, the existing charging infrastructure and a representative capacity utilisation of individual charging points were derived on a weekly basis. Further investigations are planned:
- Transfer and adaptation of the methods to other study areas
- Derivation of quality characteristics (e.g. comparison with data from the Federal Network Agency)
- Investigation of the correlation between charging station utilisation and environmental characteristics (e.g. based on land use, building types or opening hours)
- Development of a representation model to describe geographical locations as a basis for location analyses (e.g. through the use of spatial embeddings based on current deep learning approaches)
Your profile
- Ongoing studies in computer science or geoinformatics
- Planned compulsory internship or final thesis
- Very good knowledge of Python
- Quick comprehension with a goal-oriented and independent way of working
We look forward to getting to know you!
If you have any questions about this position (Vacancy-ID 2146) please contact:
Jens Kersten
Tel.: +49 3641 30960 122
