The recent Earth Observation missions like (ESA Copernicus – Sentinels, ASI PRISMA and COSMO-SkyMed, and future IRIDE constellation) make available databases of long, dense and worldwide image time series. The data have complex spatio-spectro-temporal behaviors and variability, and they show irregularities and misalignments, yet they allow for a wide range of downstream tasks. Candidates will be requested to develop novel methodologies within the artificial intelligence framework for effectively and efficiently process image time series for semantic segmentation, target detection and change detection along and across multiannual series of data. Methodologies like foundational models, machine learning, deep learning, multitask learning, enforcement learning, explainable AI, etc. will be considered. Besides the requirements established by the rules of the ICT school, preferential characteristics for candidates for this scholarship are: – master degree in Electrical Engineering, Communication Engineering, Computer/Data Science, Mathematics or equivalents; – background in artificial intelligence, image/signal processing, remote sensing, passive/active sensors.