Master's thesis: Graph Neural Networks for Algorithmic Machine Learning

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 monitoring and allocating resources in future computer and communication networks. The unit conducts projects together with industry and academic partners from Sweden and across the world.

Background and Purpose
Graph Neural Networks (GNNs) have emerged as one of the main subfields of deep learning within the machine learning community. Among their advantages are their capability to process graph-structured data, their invariance to input and output dimension, and their capability to leverage topological structure. On the other hand, many real-world resource allocation problems can be traced back to traditional combinatorial optimization problems, which can be seamlessly represented over graphs.

Thesis Description
The aim of this thesis is to explore the applicability of the most recent developments in graph representation learning and GNNs for traditional combinatorial optimization problems. Possible avenues to explore include the interaction between GNNs and neural algorithmic reasoning, large language models (LLMs), and generative flow networks, among others. The expected outcome of the thesis is a structured analysis and experimentation of selective approaches for a subset of traditional combinatorial optimization problems. 

• Duration: 6 months of full-time work (with potential for extension).
• Application: as soon as possible, or at the latest by December 8
th, 2025.
• Start date: as soon as possible, or by January 2025 at the latest.
• Scope: 30 hp.
• Location: RISE Computer Science, Kista, Stockholm. Option to partially work remotely.

Who are you?
We expect you to have strong solid knowledge of machine learning theory, deep learning architectures, good programming skills (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/resume, recent transcript of records, and a code excerpt (example of a code file written by you). Candidates are encouraged to send in their application as soon as possible but at the latest by the 30th of November 2024. Suitable applicants will be interviewed as soon as applications are received.

Keywords: Master thesis, machine learning, graph-neural networks, algorithmic ML, deep learning, 
combinatorial optimization, RISE, Stockholm