Categoria
Investigador
Unidade Orgânica
Department of Mechanical Engineering
Email
jorgebandeira@ua.pt
Ciência ID
B715-CB67-C2A2
ORCID iD
0000-0002-4859-4405

Jorge M. Bandeira is an Assistant Researcher at the Centre for Mechanical Technology and Automation (TEMA) at the University of Aveiro. His research focuses on traffic and emissions modeling, eco-navigation, traffic assignment and network optimization, and Cooperative Intelligent Transportation Systems (C-ITS) impacts, with a strong emphasis on mobility data and sustainable transportation solutions. He has a proven track record of success in securing research funding, having gathered more than 10 million EUR for cooperation and research projects.

He has served as Principal Investigator and leader of several consortia, including the European projects CISMOB and PriMaaS, and the national consortium InFLOWence. Currently, he is the Principal Investigator of the European projects SPOTLOG, EMBRACER, TenConneT, and SMARTAUTO.

Since 2018, Jorge has also taken on the role of Vice Coordinator of the TEMA mobilizing project "Technologies for Well-Being." He 1 contributes his expertise as an evaluation expert for international funding programs, such as COST Action and Horizon 2020. His application to the Individual Call to Scientific Employment Stimulus (FCT CEEC 2018) for an assistant researcher position was recommended for funding in 2019. Jorge's academic background includes a PhD in Mechanical Engineering from the University of Aveiro, completed in collaboration with Old Dominion University, VA, USA, with a thesis on a road traffic information platform for energy and emissions savings. He also holds graduate and master's degrees in Environmental Engineering and Sustainable Energy Systems.

He was awarded the European Patent Office Innovation Contest in 2013 for his project in the "smart transport and traffic management systems" category, focusing on a framework for assessing eco-routing and traffic management strategies. Recently submitted an international patent related to improved operational perfomance of autonomous vehicles.

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