PhD candidate on the topic of “Multi-scale Semantic Understanding of the Built Environment”

Updated: about 1 hour ago


12.10.2021, Wissenschaftliches Personal

The TUM Professorship for Data Science in Earth Obervation is seeking a full-time PhD candidate on the topic of “Multi-scale Semantic Understanding of the Built Environment”.

For our team, we are looking for a full-time


PhD candidate on the topic of “Multi-scale Semantic Understanding of the Built Environment”
About us

The TUM-Professorship for Data Science in Earth Observation develops innovative methods for information extraction from remote sensing data in close cooperation with the Department EO Data Science of the Remote Sensing Technology Institute of the German Aerospace Center (DLR). For this international, exciting, and cutting-edge environment, we are looking for a PhD candidate on the topic of semantic understanding of the built environment using machine learning technologies. This PhD position is part of the project “Artificial Intelligence for the automated creation of multi-scale digital twins of the built world”, which is funded via the Georg Nemetschek Institute of Artificial Intelligence for the Built World and conducted in collaboration with a range of other TUM chairs from the Geodesy and Computer Sciences domains.

Tasks Your duties will include:

  • Literature research
  • Designing, implementing, and evaluating novel machine learning approaches to retrieve buildings in 3D, building settlement types, and distribution of construction sites at very high resolution from big geospatial data
  • Exchange with our scientific partners
  • Publishing the developed approaches in international journals and conferences
Requirements

Promising applicants have:

  • A master’s degree in Computer Science, Geodesy, or related discipline
  • Very good programming knowledge, preferably in Python
  • Experience with state-of-the-art machine learning or data science technologies
  • Experience with remote sensing data is a plus
  • Solid command of the English language both in written and spoken form (German language is a plus)
What we offer

We offer the possibility to join a successful research group with outstanding international reputation (see www.sipeo.bgu.tum.de). Since the Professorship is established as a joint venture between TUM and DLR, it offers the attractive combination of university-style fundamental research directly linked to practically relevant major projects and pioneering satellite missions. Depending on the applicant’s profile and qualifications, the salary of the position will follow the TV-L pay scale up to E13. The Technical University of Munich wants to increase the number of female employees, i.e. qualified female candidates are explicitly encouraged to apply for this position. Severely disabled candidates will be preferred if they are essentially similarly qualified and suitable for the position. The position is limited to 3 years with option of extension and can basically also be a part-time position.


Interested?

Interested candidates please send their documents, including CV and documentation of their academic education to anna.kruspe@tum.de.

Technical University of Munich
Data Science in Earth Observation
Prof. Dr. Xiaoxiang Zhu
Arcisstraße 21, 80333 München, Germany
Tel. + 49 89 289 22659
xiaoxiang.zhu@tum.de
https://www.asg.ed.tum.de/sipeo/

Data Protection Information:
When you apply for a position with the Technical University of Munich (TUM), you are submitting personal information. With regard to personal information, please take note of the Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung. (data protection information on collecting and processing personal data contained in your application in accordance with Art. 13 of the General Data Protection Regulation (GDPR)). By submitting your application, you confirm that you have acknowledged the above data protection information of TUM.

Kontakt: anna.kruspe@tum.de


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