Project Scientists in new center for Machine Learning in Earth Observation (ML4Earth): Benchmarking, HPDA/HPC support, Education

06.12.2021, Wissenschaftliches Personal

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 educational opportunities 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 seeking three project scientists for benchmarking, HPDA/HPC support, and education (e.g. MOOCs, organizing workshops, facilitating community building).

Requirements:

• Completed university degree in computer science or applied mathematics, remote sensing, geophysics, physics, or related areas; other areas are welcome if added experience in topics of the project can be demonstrated
• For benchmarking: Experience in big data management, preferably Earth Observation Data
• For HPDA/HPC: Experience in setup, management, and user support of HPDA computing systems; experience in optimizing algorithms for HPC systems
• For Education: Teaching/training experience in Machine Learning and/or Earth Observation; experience organizing large workshops; experience with creating MOOCs is a plus
• Very good communication skills (oral and written) in English.
• Ability to work in a highly interdisciplinary and international group

Kontakt: zhulab@lrg.tum.de

Mehr Information

https://www.asg.ed.tum.de/sipeo/home/