© 2023 SMARTNESS 2030
This PhD student will focus on optimizing, running, and evaluating distributed learning mechanisms in vehicular network environments. The main objective is to investigate how vehicles can contribute to efficiently collect and process data to generate knowledge about many aspects in a smart city, such as traffic behavior and improvement through automated intersections, objects recognition and tracking, collision avoidance, pollution, and weather monitoring, and so on.
Monthly FAPESP scholarship:
First year: R$ 5.520,00
Second to fourth years: R$ 6.810,00
Plus 20% annually for research contingency funds
(Optionally) 1-Year Research Internship Abroad (BEPE) at a partner international institution.
[1] Bonawitz et al. Towards Federated Learning at Scale: System Design. Proceedings of Machine Learning and Systems 1 (MLSys 2019).
[2] Tang et al. Comprehensive survey on machine learning in vehicular network: technology, applications and challenges. IEEE Communications Surveys & Tutorials.
© 2023 SMARTNESS 2030