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Advertising division: IEK-11 - Helmholtz Institute Erlangen-Nürnberg Renewable Energy Production
Reference number: 2018-387

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The newly founded "Helmholtz-Institute Erlangen-Nürnberg for Renewable Energies" (HI ERN) is as IEK-11 part of Forschungszentrum Jülich (FZJ) and works in close collaboration with the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and the Helmholtz-Zentrum Berlin (HZB). The research in HI ERN is focused on the areas of innovative materials and processes for hydrogen technologies, photovoltaic energy and solar fuels, therefore covering novel approaches for the conversion and storage of CO2-neutral energy. More information about HI ERN can be found at www.hi-ern.de.

The successful candidate will be part of the research unit “High-Throughput Methods in Photovoltaics” of the HI ERN led by Prof. Dr. Christoph J. Brabec. The teams within this research unit focus on the development on high-throughput methods for materials development, production and system characterization in the field of photovoltaics in order to enable the deployment of photovoltaics (PV) on the Multi-Terawatt-scale. This position will be part of the team “High-Throughput Characterization and modelling for PV”. We utilize novel state-of-the-art characterization techniques, modelling and metrology methods in order to understand the reliability and performance of photovoltaics in the field and in the lab. The goal of the research is to develop effective operation and maintenance methods of PV systems on the Terawatt-scale, in order to secure a sustainable renewable energy supply.

We are looking to recruit a

Data Scientist for Machine Learning and Pattern Recognition for PV Data Analysis

Your Job:

  • Automated image analysis of drone based infrared and luminescence images of PV modules obtained from PV systems in the field
  • Automated defect recognition (e.g. crack detection) in the images by means of machnine learning, neural networks, AI etc.
  • Data mining of monitoring and possibly production data of PV modules and systems in order to deduce typical failure patterns
  • Automated correlation detection in data sets from various sources (e.g. imaging data and power measurements)


Your Profile:

  • Graduate of a university with excellent academic degree or doctorate in Computer Science, Physics, Electrical Engineering or a relevant discipline
  • Previous experience in programming and pattern recognition and one (or more) of the following is desirable: Machine learning, deep learning, neural networks, artificial intelligence, automated image analysis, etc.
  • Background in setting up, maintaining and using data bases
  • Strong background in statistical data analysis
  • Strong interest in pursuing research in a multidisciplinary project related to renewable solar energy
  • Excellent organizational skills
  • Ability to show initiative and work independently
  • Excellent cooperation and communication skills and ability to work as part of a team and ability to lead a team
  • Excellent skills in spoken and written English
  • Basic knowledge of German


Our Offer:

  • A lively scientific environment within the subinstitute and possibilities for cooperation with excellent partners at the Friedrich-Alexander-Universität Erlangen-Nürnberg, the Forschungszentrum Jülich, the Helmholtz-Zentrum Berlin and numerous partners abroad
  • An excellent international work environment to perform sound, high-quality research at the international level
  • Daily, in-depth experience in one of the most relevant and interdisciplinary topics in the field of renewable energy
  • An active involvement in project administration, acquisition and reporting is possible but not mandatory (Senior Scientist)
  • Active participation in project meetings, as well as on national and international conferences to present the results and to develop further competences
  • Interaction and cooperation with world-leading industrial partners
  • Strong support and mentoring for setting up a future career in science and/or the industry
  • Limited position for 3 years
  • Full-time position with the option of slightly reduced working hours or if not yet pursued already the successful candidate will have the possibility to enroll as a PhD candidate in order to obtain a PhD on this topic at the Institute of Materials for Electronics and Energy Technology at the Friedrich-Alexander-Universität Erlangen-Nürnberg in part-time
  • Salary and social benefits in conformity with the provisions of the Collective Agreement for the Civil Service (TVöD). Depending on the applicant\'s qualifications and the precise nature of the tasks, salary grade EG13 TVöD-Bund


We also welcome applications from disabled persons.

Additional Information

We look forward to receiving your application, preferably online via our online recruitment system on our career site until 21.01.2019, quoting the above-mentioned reference number.

Questions about the vacancy?
Contact us by mentioning the reference number 2018-387: career@fz-juelich.de
Please note that for technical reasons we cannot accept applications via email.