Location: Kista, Stockholm

Preferred starting date: Jan. 2026

Extent: 1-2 student, 30hp.

About the company

Founded in 1987, Huawei Technologies is one of the fastest growing telecommunications and network solutions providers in the world. At Huawei Technologies, we look for people who share our vision: to enrich life with communication. We are a leading supplier of next generation telecom networks and currently serve 37 of the world’s top 50 operators. Our people are committed to providing innovative products, services and solutions and understand it as their mission to create long-term value and growth potential for our clients.

The Huawei office in Sweden is the leading overseas R&D office in Huawei, and the Wireless Algorithm group at Huawei Sweden drives innovation for the Huawei Wireless RAN product. We work on both advanced receivers and on Radio Resource Management algorithms, for both LTE and 5G.

Thesis description

This master’s thesis investigates data-driven approaches for the MIMO precoding problem using advanced neural architectures such as Transformers and graph neural networks (GNNs). In MIMO systems, precoding refers to the signal processing technique used at the transmitter to shape the transmitted signals so that they can be more effectively separated and decoded at the receivers, improving overall data rates and reliability. Traditional optimization-based methods for precoding are often computationally demanding and limited in their ability to adapt to changing conditions.

Recent advances in deep learning offer an alternative through models that can learn these mappings directly from data. Both Transformers and GNNs are particularly promising—Transformers excel at capturing global dependencies through attention mechanisms, while GNNs naturally represent relational structures among antennas and users. This thesis explores the design, training, and evaluation of these architectures—individually or in combination—to develop intelligent, high-performance solutions that bridge modern machine learning and signal processing.

Your Profile

• Master student in Electrical Engineering, computer science or equivalent.
• A solid theoretical background in areas such as mathematics and signal processing. Knowledge of linear algebra, probability, and optimization.
• Experience in machine learning and AI, familiar with deep learning models.

• Good knowledge in simulators, proficiency in Python and PyTorch.

For more information regarding this opportunity, please contact:

Nima Najari Moghadam, nima.nm@huawei.com