Student Assistant (m/f/d) - Dynamic Charging Infrastructure (Jena)

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. This role focuses on processing dynamic data to derive utilisation metrics, whilst the other role involves the collection and preparation of static data (e.g. location & configuration; see vacancy 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: Connecting a time-series database to several live data interfaces to record the current utilisation of the Germany-wide charging infrastructure Deriving utilisation trend lines through parallel processing of the recorded time-series data Tests for pipeline stability and error handling Your qualifications Currently studying for a Master’s degree (.) in Computer Science or a comparable subject area Very good knowledge of Python Initial practical experience with time-series databases, ETL pipelines and parallelisation Experience with push-based data streams would be an advantage Quick learner 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 5826 ) please contact: Dr. Friederike Klan Tel.: +49 3641 30960 555