EQAI 2023 – European Summer School on Quantum AI

AILAB-Udine is proud to be one of the organizers of the 2nd European Summer School on Quantum AI, to be held in Udine on May 29 โ€“ June 01, 2023. Find the latest updated information in the official website:http://eqai.eu The summer school will take place in Udine (Italy) on May 29 – June 1, 2023, but it can also be followed remotely. The main topic of this edition is โ€œQuantum Machine and Deep Learningโ€. Deadlines for application are: โ–ถ on-site: April 29, 2023, โ–ถ remote: May 15, 2023. All the speakers will be there in person to make the experience more immersive and interactive. The program will include lectures, tutorials, and dissemination opportunities. The participants may also present their own research work during a dedicated poster session, having the opportunity to interact and discuss with their peers. More information can be found on the dedicated website: http://eqai.eu/ Feel free to share this invitation with anyone interested in Quantum Computing, Quantum […]

AI for Forestry Applications

This research project focuses on the application of machine and deep learning methods for forestry applications. In particular, the main focus is forest growing stock prediction in the Friuli Venezia Giulia region (Italy), but the developed methods can be applied to produce estimations of biophysical forest attributes on any large territory. This study will take into account different sources of data such as Forest inventory data from Nationla surveys, multispectral satellite images, climatic data and various environmental features collected through different services. Several methods will be applied to produce a forest-growing stock volume map, which will be useful to create management plans for forestry areas in the region. Traditionally, the growing stock is considered an important indicator of forest health and productivity. The growing stock is estimated through forest inventory under which both qualitative and quantitative parameters are recorded to know the overall health of growing forests. So, we will produce the results that can be considered as a basis […]