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M.Sc. Thesis: Machine Learning for Network Resource Management

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. 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 Neural Networks and Reinforcement Learning for generating interrogation schedules in Time-Division Multiple Access (TDMA) setting for backscatter sensor networks with emphasis on scalability and adaptability to larger problem instances.


  • Start Time: As soon as possible.
  • Scope: 30 hp.
  • Location: RISE Computer Science, Kista, Stockholm. Option to partially work remotely.

Who are you?

We expect you to have a strong and solid knowledge of machine learning, good programming skills (mostly in Python) and an interest in solving complex problems.         

Welcome with your application!

To know more, please contact Daniel Pérez (daniel.perez@ri.se, tel 073 806 2917). Applications should include a brief personal letter, CV, recent transcript of records, and a code excerpt (example of a code file written by you, or your GitHub repository link). Candidates are encouraged to send in their application as soon as possible but at the latest by the 15th of January 2024. Suitable applicants will be interviewed as soon as applications are received.


Keywords: Master thesis, Machine Learning, Graph Neural Networks, Reinforcement Learning, IoT, Backscatter Sensor Networks, RISE, Stockholm

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Student - examensarbete/praktik


Daniel Pérez



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