About the SnT The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. The Interdisciplinary Centre for Security, Reliability and Trust ( SnT ) at the University of Luxembourg is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services. We play an instrumental role in Europe by fueling innovation through research partnerships with industry, boosting R&D investments leading to economic growth, and attracting highly qualified talent.Welook forresearchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services & Applications. This industrial PhD position is part of the "Autonomous systems for Land, Air and Space" (ATLAS) IPGB Programme co funded by the FNR, SnT and a consortium of industrial partners and administrations, including Volvo Buses e Bus Competence Center, Emile Weber, GomSpace, Gradel, IEE, Nexxtlab, Telindus and Ville de Luxembourg. For more information on the ATLAS IPBG Programme, see here: https://edu.lu/wwpy7 Your role The successful candidate will join the PCOG Research Group, led by Dr. Gregoire Danoy, in collaboration with the UBIX Research Group, led by Prof. Raphal Frank. Thanks to the partnership with the Volvo e Bus Competence Center the student will work with the latest technologies in the field of electrification, address real world challenges and apply the findings in practice. Frequent training visits to Volvo headquarters in Gothenburg, Sweden, are foreseen. This PhD project addresses the question of the optimal setup on an electric bus, with primary focus on the battery and charging methods. The current lack of knowledge regarding energy consumption models has led to the prevalent but inefficient strategy of oversizing batteries, resulting in unnecessary costs and environmental externalities. The project aims to develop advanced learning mechanisms, using real world data from electric bus fleets and applying federated learning to create energy consumption models that account for variable operating conditions, such as ambient temperature and door opening patterns. Additionally, it will explore how automation both at the local (bus) and global (fleet) levels can contribute to energy reduction, investigating whether isolated or system level automation offers the greatest benefits. You will be required to perform the following tasks: Carrying out research in the predefined areas Disseminating results through scientific publications Communicate and interact with the partner''s team to collect requirements and report results Implement and open source proof of concept software tools Collaborate with other researchers of the IPBG ATLAS programme For further information, please contact Your profile Qualification: The c