Cities of Tomorrow and especially their city centre will be greener, without CO2 emissions of urban transports with a greater place to the Eco-mobility. This change has already begun, pedestrian and cycle route are densifying and the role of infrastructures appears quite naturally as a prerequisite to enhance a shift to these soft transport modes. Indeed, many obstacles (high-volume traffic roads, rivers, etc.) can hinder cyclists and pedestrians flows. The NEMELIFT project aims to develop a crossing technology fostering the mobility by being inexpensive, fast to setup and agile. The agility of the solution makes its originality and aims to address urban mobility issues in an innovative way. The project gathers 4 international research laboratories specialized in vibrations and contact (SUPMECA, France) and in active control systems for civil engineering applications (NCNU, NCKU, NCUT and NUK Taiwan), and 1 industrial partner (LOCAPAL) specialized in temporary footbridges and steel structures. The NEMELIFT project is planned for 48 months. It is divided into 3 technical Work Packages (WP) dedicated to the development of numerical tools for the robust design of light slender assembled structure (WP1, Leader SUPMECA), the control of the bridge deflection using continuous parameters identification (WP2, Leader NCNU) and the development of a Digital Twin of this innovative technology of bridge (WP3, Leader SUPMECA). Interns participating in this program have the opportunity to learn and experience novel measurement, automation and control, and digital twin technologies. Students with a machine learning background are also welcome. In this internship, you will be responsible for and assist in completing the following tasks: (1) Under the guidance of the instructor, assist in completing the calibration of the image measurement system, improve its measurement frequency in real-time multi-point measurement, and conduct experimental verification. (2) Use machine learning to predict the control strength from the bridge deformation model, and apply it to the bridge model of National Chi Nan University to verify its effectiveness.