PhD, PostDoc, Group Leader and Guest Professor Positions in AI4EO

PhD, PostDoc, and Group Leader (Wissenschaftliche/r Mitarbeiter/in)

Zhu lab is a joint venue of the Professorship for Signal Processing in Earth Observation at the Technical University of Munich [] and the Department EO Data Science of the Remote Sensing Technology Institute of the German Aerospace Center (DLR) []. We develop innovative signal processing and machine learning algorithms to extract geo-information from big geospatial data, ranging from remote sensing satellite data and even social media data. As downstream applications, we provide large scale and highly accurate geo-information to address societal grand challenges, such as monitoring the global urbanization, climate research and supporting the sustainable development goals of the United Nations. Our lab offers currently several open positions for outstanding PhD Candidates, postdocs, and senior scientists at either the Technical University Munich (TUM) or the German Aerospace Center (DLR). We also have open positions for outstanding research engineers.

Topics of particular interest to the group include:

-    Earth Observation and Computer Vision

-    Machine Learning/Deep Learning

-    Unsupervised/weakly Supervised Learning

-    Uncertainty Analysis, Interpretation and Reasoning of Deep Neural Networks 

-    AutoML

-    Anomaly and Change Detection Methods

-    Geo-information Extraction from Social Media Data

-    Natural Language Processing

-    Large-Scale Data Mining and Knowledge Discovery in Earth Observation

-    Big Data Management

-    High-performance Computing

-    Statistical Learning, Modelling, Spatial and Temporal Analysis of Geographical Observations

-   Geo-referencing, Digitalization, Building and Maintaining Large Relational Data Bases and Geo-databases, Publishing Geo-services (i.e. Web Map Services)

Demonstrated hands-on experience in one or more of these areas is a requirement. Postdoc applicants should have an excellent publication record and a PhD in machine learning, computer science, statistics, remote sensing, mathematics or a related discipline. Research engineer applicants should have excellent coding skills, as well as practical skills in data science and/or deep learning, and experience with scripting and running large-scale experiments.

Application materials comprise:

-       CV

-       Full set of transcripts

-       Statement of purpose

-       Briefly state what drives you and what are your goals in applying to the SiPEO lab

-       Names for at least 2 reference letter writers

         For each reference, please include name, title, and email address.

         References should expect to be contacted for a reference letter.

Please submit these documents to Please kindly consider that due to the high requests, we will not be able to consider incomplete applications.

AI4EO Guest Professors

In the framework of the BMBF funded German International AI Future Lab AI4EO, we have also a couple of slots free for guest professors with a pay scale from W1 to W3. Should you be working on any of the three topics 1) reasoning; 2) uncertainty and 3) Ethics in AI4EO and be interested in visiting us in Munich for 18 to 36 month, please kindly in direct contact with Prof. Dr. Xiaoxiang Zhu (

About Us: 

The Technical University of Munich (TUM):

The Technical University of Munich (TUM) is one of Europe’s top universities. It is committed to excellence in research and teaching, interdisciplinary education and the active promotion of promising young scientists. The university also forges strong links with companies and scientific institutions across the world. TUM was one of the first universities in Germany to be named a University of Excellence. Moreover, TUM regularly ranks among the best European universities in international rankings.

German Aerospace Center (DLR):

As a member of the Helmholtz Association of German Research Centers, the German Aerospace (DLR) employs more than 8000 people at 20 locations. The department “EO Data Science” at the Remote Sensing Technology Institute (IMF-DAS), located at the DLR in Oberpfaffenhofen is 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 deliver crucial geo-information to address social grant challenges, such as urbanization and climate change.

Resources and opportunities for collaboration:

  • Both at TUM and DLR, we are equipped with state-of-the-art computational resources such as DGX servers. In addition, we are closely collaborating with the Leibniz Supercomputing Centre (LRZ), which provides us with access to one of the most powerful supercomputing environments in Europe.
  • Helmholtz AI: The Helmholtz AI [link] platform aims to enhance the research within the Helmholtz Association with applied AI methods. For that each research area of Helmholtz operates HAICU units to work on short, medium and long term AI projects. IMF-DAS operates the local Helmholtz AI unit “MASTr: HAICU Munich @ Aeronautics, Space and Transport”. It consists of a Young Investigator Group (YIG) in Earth observation and an AI Consulting Team, providing the expertise from Earth Observation, robotics, computer vision and an HPC/HPDA support unit. Currently we are looking for an enthusiastic Head of the YIG in the field of Large-Scale Data Mining in Earth Observation, and two PhD students.
  • Future AI Lab on Artificial Intelligence in Earth Observation: For the BMBF-funded Future Lab on Artificial Intelligence in Earth Observation (AI4EO), we are looking for one science manager and two PostDocs to form the backbone team of the lab starting from May 1, 2020. The Future Lab will bring together 12 highly renowned senior scientists and dozens of junior scientists from across the globe to carry out cutting-edge AI4EO research that will help to bring AI4EO to the next level. The research topics include but are not limited to reasoning, uncertainty and ethics in AI4EO.

Signal Processing in Earth Observation
Prof. Xiaoxiang Zhu

Technische Universität München
Arcisstr. 21
D-80333 München


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