Internship - Development and test of a machine learning solution to support the rehabilitation of drinking water networks

Working hours

40 h per week


6 month


520,00 € per month



Kompetenzzentrum Wasser Berlin (KWB) is a non-profit company for applied research and innovation founded in 2001 with over 35 employees. We develop research projects by crosslinking future topics like digitalization, resource efficiency, smart city and climate resilience with the traditional fields of the urban water cycle ( In addition, KWB promotes the active transfer of knowledge to experts, municipal stakeholders, research partners, as well as to the interested public through public relations and the organization of events.

Your opportunity to make an impact:

As part of an interdisciplinary research team within the “Urban Systems” group, you will work on research projects at the interface of hydroinformatics and engineering, contributing to climate adaptation and urban infrastructure resilience.

Your focus will be to support us to develop and test machine learning approaches for asset management  in drinking water distribution system (DWDS). In particular, you will contribute to the uptake of our data-driven solution for asset management for sewer networks (SEMAplus) and help us to transfer it to the specifics of DWDS.

SEMAplus for DWDS will provide rapid and accurate information as well as a foundation for targeted short-term maintenance and long-term investment planning. To accomplish this, SEMAplus requires only a limited amount of pipe data (age, material, diameter, etc.), historic data on pipe failues, and, if available, data on other ageing-relevant environmental factors. The tool will help water utilities to save on resources, costs and time. SEMAplus for DWDS will also simplifies communication between decision-makers thanks to its easily understandable interactive dashboards.

Specific tasks include:

  • Literature review on machine learning algorithms for asset management in DWDS
  • Explorative data analysis and applying machine learning and planning tools for asset management for drinking water pipes
  • Data engineering, data preparation, data analysis and interactive data visualization
  • Close cooperation and networking with research partners, e.g., the water utility of the city of Lausanne (Switzerland).

Your areas of knowledge:

Must have

  • Ongoing MsC in the field of (Hydro-)Informatics, Computer or Data Science, Civil Engineering, Environmental Engineering or similar
  • Solid methodical approach in data science, statistics, machine learning and modelling
  • Excellent written and verbal communication skills in English
  • Enthusiasm for working in an interdisciplinary and international environment
  • Interested in water management and infrastructure planning topics

Nice to have

  • Experience in programming (preferably in R and/or Python) and/or software development
  • Professional working proficiency in German


  • 6 months experience in a leading research institute in Berlin, the capital city of Berlin
  • Join a leading international research center addressing the most pressing challenges of water management, environmental protection and smart city
  • Work in an interdisciplinary team of researchers and experts
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