Course paper / final thesis

Detection of wake vortices in LIDAR - measurements using artificial neural networks

Starting date

1 january 2020

Duration of contract

6-12 months


up to the German TVöD 5

Type of employment

Full-time (part-time possible)

"Cutting-edge research requires excellent minds – particularly more females – at all levels. Launch your mission with us and send in your application now!" Prof. Pascale Ehrenfreund - Chair of the DLR Executive Board

Flying aircraft generate a pair of co-rotating vortices, the so-called wake vortices. In the final approach, wake vortices can endanger following aircraft; they continue to be the main cause of existing aircraft separation at airports. Monitoring wake vortices at airports is complex both in terms of the instruments used and the processing. In recent years, LIDAR (Light Detection And Ranging) has established itself as a suitable instrument for measuring the aircraft wake flow. However, good evaluation algorithms of the LIDAR signal are very time-consuming, so that an operational application at airports for real-time observation is out of the question.

Thanks to the current developments in the field of automated image recognition through the use of artificial neural networks, it will be possible to perform real-time observation at airports in the future.

The Institute of Atmospheric Physics at DLR Oberpfaffenhofen aims to reduce the risk of wake vortices and thus to reduce aircraft separation.  During a recently completed measurement campaign lasting six months at Vienna International Airport, an extensive data set was generated and evaluated in large parts. The evaluation mainly relates to the location and the strength of the wake vortex. The evaluations are now to be used to set up an artificial neural network and to train with the existing data. The trained network will enable real-time monitoring of wake vortices with LIDAR systems at airports and ultimately make air traffic safer and more efficient.

Your tasks:

  • setting up an artificial neural network
  • training of this ANN with already gained data
  • evaluation and verification

Your qualifications:

  • Master's programme: mechanical engineering, aerospace engineering, computer science, physics, mathematics, meteorology, engineering, etc.
  • knowledge of a programming language, preferably Python
  • good knowledge of the German or English language, both written and spoken
  • at best case experience with neural networks, data stream oriented programming languages such as TensorFlow

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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Technical contact

Anton Stephan
Institute of Atmospheric Physics

Phone: +49 8153 28-2566

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Vacancy 45807

HR department Oberpfaffenhofen

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DLR site Oberpfaffenhofen

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