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Design, development and implementation of a neural network for the analysis of photometric data from transit surveys

Starting date

1 March 2019

Duration of contract

until 31 December 2021

Remuneration

According to the German TVöD 13

Type of employment

Full-time

"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

Your mission:

The Institute of Planetary Research carries out and supports research programs on the internal structure, formation and evolution of the planets, their moons, and asteroids and comets. Techniques employed include remote sensing and in-situ investigations using instruments carried on spacecraft, astronomical observations from the ground, theoretical modeling, and laboratory experiments.

The department on Extrasolar Planets and Atmospheres investigates the following fundamental questions: How do planets and planetary systems form and evolve? What makes a planet habitable? Is there life in other planets or moons and how can we detect it? The search for extrasolar planets and their characterization provide answers to these questions.
The successful analysis of large time series of photometric data from transit surveys is limited by the presence of residuals of instrumental noise. These noise sources complicate the detection of the small signal of transiting Earth-sized planets. Machine learning techniques have the potential to discriminate between genuine planetary signals and instrumental effects more effectively than classic detrending methods. It has been shown that Machine Learning Methods can compete with classic transit search algorithms, providing a higher yield of detections, which result in a better understanding of fundamental questions on planet formation and evolution.

Your tasks:

  • design, development and implementation of a neural network for the analysis of photometric data from transit surveys
  • implementation of the training dataset
  • evaluation of the results
  • adaptation of the known methods to the task to optimize the results and application to existing data sets (for example, public data from the Kepler mission)
  • development and implementation of scientific algorithms based on Big Data techniques to the detection of transiting extrasolar planets (for example, for the PLATO mission)

Your qualifications:

  • graduate/Master degree in physics, mathematics or informatics
  • experience with the analysis of photometric light curves or with the analysis of signals (in particular, search for periodic signals)
  • knowledge of computer programming language (IDL, Python, Matlab, C...)
  • good level of English
  • ability to work in an international Team
  • good publication record in recognized international scientific journal is an asset (for applicants with background in physics, including astronomy and astrophysics)
  • ability to apply for third party funding is advantageous
  • experience supervising master-level students is a plus
  • interest in EPO (Education and Public Outreach) activities is of advantage

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 puts great value on a healthy work-life balance as well as equal opportunities for men and women. Individuals with disabilities will be given preferential consideration in the event their qualifications are equivalent to those of other candidates.

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  • You can send this job advertisement via e-mail and complete your application on a personal computer or laptop.

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Technical contact

Dr. Juan Cabrera Perez
Institute of Planetary Research

Phone: +49 30 67055-439

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Vacancy 29511

HR department Berlin

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DLR site Berlin

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DLR Institute of Planetary Research

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