Design, optimization and control of self-driving networked systems
The PhD student will work in the area of design, optimization and control of self-driving and autonomous networked systems. Self-driving networked system are inspired by the recents advances of self-driving vehicles and are envisioned to form the essential infrastructure for mission-critical services in IoT and edge cloud scenarios. Such systems should be completely autonomous, fully virtualized and software-defined, with an underlying network that is optimized for the services currently deployed and running. Autonomous networked systems should capable of monitoring and correcting itself. Basically, these systems will self-configure, monitor, manage, correct, defend, and analyze, all with very little human intervention. The self-driving networked systems will predict performance issues before users are affected. The network scenarios will typically include wireless access based on 5G technologies with wired backhaul and core. There can be a wide range of application scenarios in the area of mission-critical services, for example smart cities, autonomous vehicles, cloud robotics, eHealth, etc.
The appointment as a PhD student include own research work and other duties related to teaching, research and administration, according to the specific regulations. The PhD student will be employed at the Dept. of Electrical and Information Technology, in the research group of BroadbandCommunications. The research team is working closely with Swedish telecommunications industry and the student is expected to work in this environment. The project is interdisciplinary with scientists at the Department of Electrical and Information Technology, Department of Automatic Control, Umeå University and KTH. An important part of the student's work will be to validate theoretical methods and algorithms with experiments in large-scale test beds. This requires excellent programming skills and competence and interest in mathematics, telecommunications, control theory, machine learning, and system modelling.
To be eligible, the applicant needs a Master of Science degree in Communications, Electrical Engineering, Computer Scicence, or similar as well as proficiency in English. The overall assessment must substantiate the applicant's ability to master the research education.
The assessment is based on the applicant's ability to mature with the research education and contribute to the research group. Specific criteria include the applicant's competence and experience in programming, queueing theory, control theory and computer communications, as well as performance during undergraduate education and references. Commitment to collaborating with the group in all matters related to the application's position and contributing to a great environment is of vital importance.