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 Data Science 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
- 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:
- 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 ai@DLR.de. 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 (firstname.lastname@example.org)
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.
Science Manager (m/f/d)
The International Future Lab “AI4EO: Artificial Intelligence for Earth Observation – Reasoning, Uncertainties, Ethics and Beyond” (https://ai4eo.de/) brings renowned international organizations across the globe and a rich number of highly ranked scientists at all levels together to address three fundamental challenges in Earth observation specific cutting-edge artificial intelligence research –Reasoning, Uncertainties, and Ethics. The research carried out in the Future Lab AI4EO will not only advance Earth observation science but also make key contributions for the interpretability of AI and its ethical implications, and towards AI4EO technology transfer.
TUM serves as the host institution for the Future Lab AI4EO, represented by the Professorship “Data Science in Earth Observation” (Prof. Xiaoxiang Zhu). Consistently ranked among Europe’s leading universities, TUM has recognized the challenges emerging in the digital age and is already spearheading the advancement of Al research from fundamental stages via applied research to the study of Al’s social implications. In this context, the DLR is a strong partner. Together with LMU, Helmholtz Center Munich and Max Planck Institute for Plasma Physics, DLR and TUM established the Munich School of Data Science (MUDS) to train the next generation of “data scientists” – interdisciplinary experts in applying, adapting, and developing methods for AI and data science tailored for a broad array of research domains, including EO. In addition, DLR founded the local Helmholtz Artificial Intelligence Cooperation Unit (HAICU) – MASTr (Munich Unit @ Aeronautics, Space and Transport), which is providing AI expertise from EO, robotics, computer vision and HPC/HPDA support. DLR also started strategic cooperation with Leibniz Supercomputing Centre, e.g. through recently signed cooperation agreement “Terra Byte”, which shall enable the highly efficient and independent analysis of large amounts of data using the latest methods to understand global trends and their consequences. This further strengthens the already existing network of collaboration.
The Lab will be physically located at the new campus of TUM in Taufkirchen/Ottobrunn, where TUM currently has established its new Department of Aerospace and Geodesy as part of the Bavarian space initiative. Besides TUM, the area also hosts the University of Federal Armed Forces, Munich Aerospace, and several EO and space industry partners (e.g. Airbus, Siemens, and IABG), thus providing the ideal location for knowledge and technology transfer. In addition, two further institutions, namely Munich Data Science Institute (MDSI) and the TUM Institute on Ethics in AI, are strong partners, who will provide data science theoretical support and ethical guidance to AI4EO.
The highly visible position of the Lab with its internationality and the close connection to other Institutes requires a respective Management. You will be in close contact with our international Guest Professors and our motivated Young Fellows, as well as with colleagues from the partners form the German Aerospace Center and the TUM
Through this advertised position the Future lab is expected to be supported mainly through appropriate science management, as well as to seek new collaborations. You will also initiate, implement and manage new scientific projects in the field of artificial intelligence for earth observation.
In addition, independent, scientific research on the topic of AI for Earth observation will be supported.
- PhD in computer science, geoinformatics, data science, business administration, or comparable field of study
- professional experience in Earth observation and/or Data Science
- Background of general AI Methods, ideally applied on EO data
- very good knowledge in the area of scientific project application as well as the German research landscape
- good experience in the field of scientific communication ('Science Communication')
- experience in project management
- fluent command of the German language and very good knowledge of the English language, both written and spoken
- excellent communication and cooperation skills, ability to interact with scientists at different levels
- Fluent in spoken and written German language
- very good knowledge of spoken and written English language
- ability to work highly motivated and independently in a team
OTHER WELCOMED QUALIFICATIONS
- good graphic design skills with tools such powerpoint and photoshop is welcomed
- experience in website design
Payment will be based on the Collective Agreement for the Civil Service of the Länder (TV-L, up to E14 level). TUM strives to raise the proportion of women in its workforce and explicitly encourages applications from qualified women. Applications from disabled persons with essentially the same qualifications will be given preference.
Contact: Interested candidate please send your motivation letter, CV, and supporting materials with the key word Science Manager to email@example.com. For further questions on this position, please contact Dr. Anna Kruspe: firstname.lastname@example.org
10 new research, data science, and management positions at Technical University of Munich in novel center for Machine Learning in Earth Observation (ML4Earth)
AI methods, and especially machine learning (ML) with deep neural networks, have replaced traditional data analysis methods in recent years. The Technical University of Munich (TUM), together with the German Aerospace Center’s Remote Sensing Technology Institute, has created the biggest European research team on AI for Earth Observation (AI4EO) over the past few years.
Starting in 2022, we will begin establishing a national ML4Earth center of excellence with high visibility. It will conduct own research at the highest international level by tackling fundamental methodical challenges in AI4EO and their application to the European mission of a Digital Twin Earth. ML research directions will include physics-aware machine learning, reasoning, uncertainty estimation, Explainable AI, Sparse Labels and Transferability, as well as Deep Learning for Complex Structures. These novel methods will be applied to practical tasks such as predicting European water storage, quantifying permafrost thawing, sea level budget, climate and earth system modeling, soil parameter mapping, and multi-sensor segmentation, together with our partners at renowned international and national institutes such as Bonn University, Alfred Wegener Institute, University of Bristol, Leipzig University, and the German Aerospace Center.
Another important goal of the project is international community building within the AI4EO domain. Aspects of this include the creation of benchmark data sets for a wide range of application scenarios, as well as offering educational resources for interested scientists and expert workshops. Our aim is the democratization of AI4EO to enable more researchers to exploit Copernicus and other EO data sources. To establish this new center, we are currently offering:
- 6 new PhD candidates/Postdocs in the described scientific topics
- One project manager
- One team assistant (50%)
- One HPDA/HPC support scientist
- Benchmarking and education roles
For more information, please contact us under email@example.com
Ph.D. Data Science in multi-baseline SAR Interferometry
The department "EO Data Science" of the Remote Sensing Technology Institute develops modern signal processing and AI procedures for present and future Earth observation missions. It is involved in a number of third-party funded projects and in a large international network. One of our main research directions is synthetic aperture radar (SAR) and its interferometry, because SAR, as an active and coherence sensor, has the unique advantage of weather independent, globally consistent, and geodetically accurate positioning measurement over optical sensors. These play important roles for tackling large-scale grand societal challenges such as urbanization and climate change.
Therefore, the envisioned doctoral research position aims at developing advanced multi-baseline SAR interferometric algorithms for current and next generation SAR missions, contributing to solving grand societal problems. Research questions lie on how to more accurately and efficiently retrieve global geophysical parameters, such as biomass and 3D height, from current and future multi-baseline InSAR measurements. Challenges arise on how to mathematically modeling a complex process that generalizes, how to invert the model efficiently, how to tackle the big data volume, etc. Here, both physical model-based and data-driven approaches shall be taken into account, because the successful development of various SAR missions have irreversibly taken the field into the Big Data era. The research will also contribute to the development of future German SAR missions like High Resolution Wide Swath.
- Completed scientific university studies (university diploma/master's degree) in computer science, mathematics, electrical engineering, or a comparable course of study.
- Experience in SAR remote sensing / SAR interferometry.
- Good programming skills in a development language (IDL, Matlab, Python) or C/C++.
- Experience in machine learning.
- Good command of written and spoken English.
- Enthusiasm in Earth observation, and want to contribute for the benefit of our society.
- You work independently, but also highly motivated in a team and have excellent communication and cooperation skills.
Contact: Interested candidate please send your motivation letter, CV, and supporting materials to firstname.lastname@example.org