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Data Scientist - Condition Monitoring + Predictive Maintenance for Transport at Deutsches Zentrum für Luft- und Raumfahrt e.V. (Berlin, Deutschland)

Welcome at DLR.

Our Institute of Transportation Systems in Berlin wishes to employ a Data Scientist, Physicist, Mathematician, Geophysicist or similar for the Condition monitoring and predictive maintenance of transport infrastructure. 

Your mission:

Mobility has a high priority in our society. People want to reach their destination safely, comfortably and quickly. Goods must be transported cost-effectively over short and long distances. The consequences of mobility can be seen in environmental pollution, accidents and traffic jams, which increase with the ever-growing volume of traffic. These are the challenges we face at the Institute of Transportation Systems. We develop solutions for the safe and efficient mobility of the future.

Help us to shape the future of transport infrastructure! The DLR Institute of Transportation Systems is working together with practice partners on the development of the foundations for the predictive maintenance of road and rail infrastructures. Our goal is a resilient, highly available and trouble-free transport system with lower life-cycle costs. We are therefore conducting research into methods and algorithms for the detection, diagnosis and prognosis of facility conditions by means of measurement data from embedded sensors.

For this challenging work, our interdisciplinary Data Science team is seeking reinforcement in the areas of signal and data analysis, pattern recognition, machine learning and system modelling.

Your task is the analysis of measurement data, which provide information concerning the condition of selected systems and facilities of traffic infrastructure and vehicles. This includes not only pure data-driven analysis procedures but also the modelling of the investigated systems. The working collaboration will take place within the framework of national and international projects in cooperation with infrastructure operators, industry and other research institutions.

Your qualifications:

  • completed scientific university studies (diploma/master) in data science, computer science, physics, mathematics, electrical engineering, technical cybernetics, geophysics or a comparable course of studies
  • doctorate or comparable proof of scientific qualification including relevant publications (German/English) in the field of data science and applications of respective methods for engineering problems
  • deep knowledge in data science methods (e.g. supervised and unsupervised learning, time series analysis in time and frequency domain, deep neural networks, time series prediction, Bayesian networks, ...)
  • profound experience in the application of data science methods to large and/or heterogeneous data sets with time and location reference (preferably with Python)
  • several years of professional experience in research and development (e.g. in the field of condition monitoring, cyber physical systems, system identification, anomaly detection, IoT concepts, etc.)
  • project experience in science, preferably with experience in acquiring and managing nationally and internationally (e.g., EU) funded research projects
  • very good German and English language skills, both written and spoken
  • good self-organisation and a high level of independent initiative in the processing of complex scientific and technical issues
  • ability to quickly familiarize yourself with unknown circumstances and complex interrelationships including new tools and methods in data science
  • practical experience in agile working (e.g. SCRUM) is an asset
  • experience in Apache Spark is a plus

Your benefits:

Look forward to a fulfilling job with an employer who appreciates your commitment and supports your personal and professional development. Our unique infrastructure offers you a working environment in which you have unparalled scope to develop your creative ideas and accomplish your professional objectives. Our human resources policy places great value on a healthy family and work-life-balance as well as equal opportunities for persons of all genders (m/f/non-binary). Individuals with disabilities will be given preferential consideration in the event their qualifications are equivalent to those of other candidates.

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