PhD position on fusion of social media and remote sensing satellite data
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.
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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
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Über Technische Universität München
The Research Group Signal Processing in Earth Observation (SiPEO) develops explorative algorithms to improve information retrieval from remote sensing data, in particular those from current and the next generation of Earth observation (EO) missions. The improved retrieval of geo-information from EO data can be used to better support cartographic applications, resource management, civil...Mehr über die Technische Universität München