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Master Thesis Opportunity – Flow Matching for Physical Layer Wireless
2 weeks ago
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 project explores the application of flow matching — a recent generative modeling framework — to wireless physical layer tasks. Many key problems in wireless communication, such as channel estimation and MIMO detection, can be formulated as inverse problems, where the goal is to reconstruct the transmitted signal or channel state information from noisy and incomplete observations.
By leveraging flow matching, we aim to learn data-driven vector fields that transform simple prior distributions into the complex distributions of wireless channels and transmitted signals. This approach offers several advantages:
- Provides a probabilistic generative model of the channel and signal space.
- Allows solving inverse problems by learning transport maps instead of hand-crafted estimators.
- Scales naturally to high-dimensional problems such as massive MIMO and OFDM-based systems.
The ultimate goal is to demonstrate that flow-matching-based solvers can outperform or complement classical estimators in terms of robustness, accuracy, and generalization across diverse channel conditions.
Your Profile
- Master student in Electrical Engineering, computer science or equivalent.
- A solid theoretical background in areas such as mathemetics, information theory 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:
Diana Wang,
