Master Thesis: Machine Learning for Network Resource Management - Under tillsättning eller tillsatt, ej sökbar

We are looking for a dedicated master’s student to join us in the Connected Intelligence Unit at RISE.

The Connected Intelligence Unit is part of RISE Computer Science in Kista. The current research focus is on devising intelligent autonomous systems for controlling and allocating resources in future computer and communication networks. Among the group's key technologies are Internet of Things (IoT) and Edge computing. The unit conducts projects together with industry and academic partners from Sweden and across the world.

Background and Purpose
Computer and communication networks are increasingly becoming more complex and heterogeneous due to recent technological advances, such as 5G, IoT, and Edge Computing. Managing those resources implies solving optimization problems that are combinatorial in nature. Traditional methods for finding feasible solutions to such problems rely on human-crafted heuristics, which are often one-sided and sub-optimal, leading to waste of unnecessary resources. Finding optimal policies for network management is paramount for transitioning towards a more sustainable future. In recent years, the machine learning field has seen a surge in both methods applied to solving combinatorial optimization problems, and in processing graph-structured data.

Thesis Description
This thesis aims at exploring the capabilities of machine learning for solving combinatorial optimization problems in the networking domain. More specifically, on applying recent advances of Graph Representation Learning, such as Graph Neural Networks, for developing intelligent management policies in this domain with emphasis on scalability and adaptability to larger problem instances.

Terms:

• Start Time: As soon as possible
• Scope: 30 hp
• Location: RISE Computer Science, Kista, Stockholm

Who are you?
We expect you to have a good knowledge of machine learning, good programming skills and an interest in solving complex problems.

Welcome with your application!
To know more, please contact Daniel Pérez, tel 073 806 2917, Carlos Pérez-Penichet, or Thiemo Voigt, tel 010 228 43 48. Applications should include a brief personal letter, CV, and recent grades. Candidates are encouraged to send in their application as soon as possible but at the latest 28th of November 2021. Suitable applicants will be interviewed as applications are received.

Master thesis, Machine Learning, Graph Representation Learning, IoT, Edge Computing, RISE, Stockholm