Within the framework of the European Space Agency’s Jupiter Icy Moons Explorer (JUICE) and EnVision mission to the Jovian system and Venus respectively, we seek highly motivated candidates to develop innovative methodologies for radar sounder data processing, with a strong emphasis on artificial intelligence and deep learning. The research activity will focus on the design and implementation of advanced models for data enhancement, denoising, semantic segmentation, content-based retrieval, target detection, multitemporal analysis, and radar mapping, aiming at improving the information extraction and the of icy planetary bodies subsurface by radar sounder data processing. The selected candidate will be expected to develop novel machine learning methods, including deep learning architectures, self-supervised and unsupervised approaches, physics-informed neural networks, transformer-based models, and/or quantum-inspired learning techniques, capable of integrating electromagnetic propagation physics with data-driven strategies, while addressing key challenges such as noise, clutter, signal attenuation, and limited availability of labelled data. The outcomes of this research will significantly advance understanding of planetary subsurface structures and their geological and climatic evolution, and will develop transferable methodologies applicable to Earth observation.

Besides the requirements established by the rules of the ICT school, the following characteristics are preferred for candidates for this scholarship:
– master’s degree in Electrical Engineering, Communication Engineering, Computer/Data Science, Mathematics, or equivalent;
– knowledge in artificial intelligence, image/signal processing, remote sensing, and radar remote sensing.