PhD Candidate in Deep Learning for Remote Sensing

Updated: 7 months ago
Deadline: The position may have been removed or expired!

04.12.2019, Wissenschaftliches Personal

For the HGF-funded project AICORE (Artificial Intelligence for Cold Regions), we are looking for a doctoral candidate for the development of deep learning approaches for the analysis of cold regions in satellite imagery.

For our team, we are looking for a

PhD Candidate in Deep Learning for Remote Sensing

About us

The department “EO Data Science” at the Remote Sensing Technology Institute of the German Aerospace Center (DLR) in Oberpfaffenhofen is a joint venture with the TUM Professorship for Signal Processing in Earth Observation. Our mission is about developing novel signal processing and AI algorithms to improve information retrieval from remote sensing data, in particular those from current and the next generation of Earth observation missions, and to deliver crucial geo-information to address social grand challenges, such as urbanization and climate change. For the HGF-funded project AICORE (Artificial Intelligence for Cold Regions), we are looking for a doctoral candidate for the development of deep learning approaches for the analysis of cold regions in satellite imagery.

Tasks

Your duties will be comprised of:

  • development of deep learning models for change pattern identification of outlet glaciers in sequential remote sensing data
  • development of deep networks for edge detection of calving fronts of glaciers/ice shelves in satellite images
  • development of task-specific deep learning models for Firn line detection and monitoring
  • documentation of the developments and experimental results in software documentation and scientific papers

Requirements

Promising applicants have a master‘s degree in computer science, machine learning, remote sensing or similar. In detail, we expect:

  • a sound command of common programming languages, especially Python
  • experience in working in a Linux environment
  • experience in working with image data
  • solid command of the English language both in written and spoken form
  • experience in deep learning and the necessary frameworks (e.g. PyTorch, Keras, TensorFlow)

What we offer
  • high scientific professional networking as well as scientific excellence
  • internationality and diversity
  • interesting and diverse tasks, flexible working hours, salary based on the collective agreement TVöD-Bund
  • attractive work and research terms in a highly motivated lab

Interested?

Interested candidates please send their documents, including CV and documentation of their academic education to Prof. Dr. Xiaoxiang Zhu (email:ai@dlr.de)

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Kontakt: xiaoxiang.zhu@tum.de


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