The goal of the project is to design an architecture and self-learning algorithms that can optimize and guarantee deterministic QoS of individual traffic flows whilst maximizing network resource efficiency. The general QoS architecture for end-to-end communication of heterogeneous types of traffic will include routing and scheduling for each traffic type and admission control possibly with QoS renegotiations and with shaping and spacing. Ideally, the performance of the proposed QoS architecture will be evaluated by using stochastic modelling and queuing theory, in addition to simulations that will complement the mathematical analysis. Employing AI/ML methodologies are strongly encouraged. The duties include theoretical analysis, algorithm design and implementation via software based simulations, writing scientific papers and support to project management and applications.
More information about application procedure etc. is given here: https://www.miun.se/en/meet-mid-sweden-university/career/jobs/vacancy/postdoctoral-position-/
