Postdoc Position in interferometric Sar Data

The Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI) is a member of the Helmholtz Association (HGF) and funded by federal and state government. AWI focuses on polar and marine research in a variety of disciplines such as biology, oceanography, geology, geochemistry and geophysics thus allowing multidisciplinary approaches to scientific goals. - PostDoc position in "Interferometric SAR Data Processing and Analysis for Implementation in the 3D-ABC Foundation Model" (m/d/f) **Background** The large collaborative project ‘3D-ABC’, funded for three years by the Helmholtz Foundation Model Initiative, will focus on developing a Foundation Model combining large remote sensing and field datasets, large-scale AI, generative AI, and Exascale HPC to detect, quantify, and characterize key parameters of the global carbon cycle at high spatial resolution with a focus on above and below ground terrestrial carbon stocks. 3D-ABC is led by AWI and combines the resources and expertise from 6 HGF centers (AWI, GFZ, UFZ, DLR, FZJ, and HZDR). It will build strongly on HGF-associated datasets, including products from the TanDEM-X radar mission, large airborne and field-based LiDAR datasets, process-based models, and other openly available remote sensing and field datasets. The diverse data will be combined to facilitate eight downstream tasks, from land cover change and forest height, to increasingly demanding tasks such as above-ground biomass, soil carbon storage, and model uncertainty ranges, to eventually complex tasks targeting carbon impacts of disturbances, forest productivity, and carbon uptake. Training the 3D-ABC Foundation Model involves extremely large volumes of data and computational resources, requiring High-Performance Computing (HPC) on the JUWELS HPC system and the upcoming first European Exascale supercomputer, JUPITER (both at FZJ) to facilitate exceptionally high performance for AI training and deployment at the scale needed for 3D-ABC. The PostDoc position focuses on processing interferometric TanDEM-X data towards their ingestion into the 3D-ABC Foundation Model, analysing the results, and supporting the development and validation of the forest height and biomass downstream tasks of the Foundation Model. You will also be responsible for managing a suitable and efficient transfer of the processed data for FM training to the AI and HPC teams in the 3D-ABC consortium. Important requirements are experience in handling, processing, and analyzing large-scale interferometric synthetic aperture radar data, programming, and basic knowledge of AI processing methods. **Your Tasks** - Interpretation of the requirements for the training of the 3D-ABC model and downstream task computations in terms of TanDEM-X data characteristics and processing requirements in forest areas for height and biomass mapping; - Retrieve, download from DLR archives, store, and process the suitable TanDEM-X data up to the interferometric coherence and the needed auxiliary data for each scene within the coverage of interest in the format required by the 3D-ABC implementation; - Transfer efficiently the processing results between processing facilities for FM training to the AI and HPC teams in the 3D-ABC consortium; - Develop the required software data management and processing interfaces; - Analyze the results and support the development, and validation of the forest downstream tasks; - During extended full-time stays at the Microwaves and Radar Institute (DLR Oberpfaffenhofen), you will be hosted by the PolInSAR research group -Radar Concepts department, and you will be able to use the available hardware (e.g. processing servers, Terrabyte platform, etc.) and software resources. **Your Profile** - Completed doctoral degree in remote sensing, signal processing, computer science, or other closely related fields - Experience in handling, processing, and analyzing large amount of data, especially large-scale remote sensing SAR image sets - Experience in programming and in automation of image data processing - Interest in the development of interferometric SAR data sets for large-scale AI and FM training - Basic knowledge of AI processing methods and dataset requirements - Relevant publication record and experience presenting scientific results at the international level - Ability and willingness to publish research results in the peer-reviewed literature - Ability and willingness to work focused, independently, and cooperatively in a large team and with international partners - Excellent communication skills and fluent knowledge of English (spoken and written) - Experience in the presentation of results at international conferences. **Preferred Qualifications and Skills** - Experience in SAR interferometric data understanding and inversion - Experience in Deep Learning and Foundation Models - Prior experience with interferometric SAR data sets in forest regions - German language knowledge **Further Informatio