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Research assistant in in the field of machine learning

24.01.2019 - University of Osnabrück, Germany

The working group Remote Sensing & Image Processing at the Institute of Computer Science at the University of Osnabrück seeks a research assistant (salary grade E 13 TV-L, 100 %) who will aim at the development of state-of-the-art methods in the field of machine learning (e.g. deep-learning, CNN) for analyzing large data sets of multisensor remote sensing imagery. The work tasks include field work, presentation of results at conferences, peer-reviewed publications, and teaching. (3 years, foreseen start 1. April 2019)


  • Involvement in research
  • Teaching duty on bachelor and master level
  • Development of own research concepts and supporting applications for third-party funds
  • The successful candidate will have the opportunity to work towards a Habilitation

Required qualifications:

  • Above-average PhD in Geoinformatics, Geography or related disciplines
  • Sound background in remote sensing as well as Geoinformatics or spatial statistics
  • Programming skills (e.g. in R, Python).
  • Knowledge in machine learning
  • Research experiences in remote sensing, including first-author publications in international peer-reviewed journals

Desirable qualifications

  • Experience in teaching
  • Knowledge in remote sensing software (e.g. ERDAS Imagine, ENVI/IDL)
  • and GIS (e.g. QGIS, ArcGIS)
  • Knowledge of Tensorflow or PyTorch

The position is available on a full-time or part-time basis. Osnabrück University has been certified as a family-friendly university committed to helping working/studying parents and careers balance their family and work life.The university aspires to ensure equal opportunities for men and women and strives to work towards a gender balance in schools or departments where new appointments are made. If equally qualified candidates apply, preference will be given to those with special needs.

We offer a position in a young and dynamic, interdisciplinary team. Applications including a letter of motivation (one page) and full CV shall be sent electronically and in a single PDF file to (deadline February 1, 2019):
Prof. Dr. Björn Waske,