Student Assistant (m/f/d) - Static Charging Infrastructure

At the Institute of Data Sciences in Jena, we are working to make the data backbone for all of DLR’s areas of application (aviation, space, energy, transport, security) a reality. To this end, we conduct interdisciplinary research and development into methods with a focus on applications such as sustainable and circular processes, resilient supply chains, data-driven value chains and robust decision support. The methods developed in this way are put into practice in cooperation with other DLR institutes and external partners, whether as part of joint projects or through technology transfer activities.

What To Expect

The Power Forecast Mapper (PFM) is a digital forecasting tool that determines future charging demand for electric vehicles, identifies suitable locations for new charging points and assesses their revenue potential. As part of the PFM2market transfer project, the PFM prototype that has been developed is being further refined for commercial use.

Two student assistants are sought to map the existing charging infrastructure and its utilisation rates; the roles will have different areas of focus but will involve collaborative development. The focus here is on adapting existing methods and implementations to new data sources. One position involves the collection and processing of static data (e.g. location, configuration, accessibility), whilst the other involves the processing of dynamic data (utilisation metrics, see job advertisement 1234).

Applications as a team are expressly welcome: in this case, please apply individually for the respective position and state in your cover letter the name of the person with whom you are applying. Individual applications are, of course, also possible.

Your tasks

• Adapting existing implementations to new data sources as part of the following tasks:
• Acquiring and processing static information on charging infrastructure from various data sources
• Contextualising charging station data, for example by linking it to cadastral data and OpenStreetMap products
• Data cleansing and quality assurance
  
  

Your Qualifications

• Currently studying for a Master’s degree (M.Sc.) in Computer Science or a comparable subject
• Very good knowledge of Python
• Practical experience with databases, ETL pipelines, parallelisation or geodata libraries
• Quick to grasp new concepts, with a goal-oriented and independent approach to work
• Good written and spoken German and English
  
  

We look forward to getting to know you!

If you have any questions about this position (Vacancy-ID 5834) please contact:

Dr. Friederike Klan

Tel.: +49 3641 30960 555