Nowadays a huge amount of remote sensing data is available being acquired with a high temporal, spatial and spectral resolution. Those data are coming from several missions, among the others: ESA Copernicus (Sentinels), ASI PRISMA and COSMO-SkyMed, and future IRID constellation. The management and use of such data requires the design of novel solutions being able to handle long time series of dense but irregular data worldwide in the context of high-power computing systems looking both back and forth in time. Candidates will be requested to develop novel methodologies based on machine learning, deep learning, pattern recognition and artificial intelligence for information extraction, classification, target detection and change detection in long and dense time series of remote sensing images. 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 Science, Mathematics or equivalents; • knowledge in pattern recognition, deep learning, image/signal processing, statistic/remote sensing, passive/active sensors.