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PhD position on fusion of social media and remote sensing satellite data

Over the last decade, the rapid development of social media has accumulated vast amounts of data such as terrestrial images, text messages, etc. Some of them carry useful information of the local environment, such as land use, urban infrastructure, population density, etc. They can be complementary to remote sensing data obtained from satellites whose temporal and spatial coverages are dependent on the sensor platform. The general goal of the envisioned doctoral research is to develop efficient land use / settlement type classification techniques using both social media and remote sensing data as input. 
Object recognition from massive online images, as well as topic modeling of massive text messages will be exploited for extracting useful information from the social media data. Deep learning techniques and latent Dirichlet allocation (LDA) methods will be studies for the classification of images and text messages. A fusion framework will be developed to combine all the classification results. The candidate is also expected to be involved in website development for collecting social media data. 
This position is offered by Signal Processing in Earth Observation (SiPEO), German Aerospace Center (DLR) and Technical University of Munich (TUM), whose mission is to develop explorative algorithms to improve information retrieval from remote sensing data, in particular those from current and the next generation of Earth observation missions. The PhD work will be carried out jointly with the Remote Sensing Technology Institute, DLR (DLR-IMF) and TUM-SiPEO. 

- Master in Earth Sciences, Maths, Physics, Computer Science or equivalent 
- Have or acquire during the research an in-depth knowledge of programming 
- Being creative and passionate 

The scholarship is awarded for a three-year period, with possible extension of up to 1 year. The monthly salary is based on the DAAD scholarship standard. Additional funding for conferences and publications is granted. Optional academic exchange is negotiable. Interested candidates should submit a full curriculum vitae and a cover letter together with academic records to the email address given below. 

Contact detail

How to apply:
Send application to
Contact person: 

Prof. Dr.-Ing. Xiaoxiang Zhu 

German Aerospace Center (DLR) 
Remote Sensing Technology Institute 
Oberpfaffenhofen, 82234 Wessling 

Technical University of Munich (TUM) 
Signal Processing in Earth Observation (SiPEO) 
Arcisstr. 21, 80333 Munich 

Email: xiao.zhu@dlr.de 
Homepage: http://www.sipeo.bgu.tum.de/

Job profile

Working hours
Contract duration
Type of job
PhD Project
Work experience
job experience is not required
Germany (Bayern)
Working place
80333 München
Area of expertise
Geo Sciences, Mathematics, Computer Sciences