Master Thesis Opportunity: Deep In-Context Learning (ICL) for Wireless Communication

Om jobbet

About Huawei Sweden R&D
Founded in 1987, Huawei Technologies has grown into a global leader in telecommunications and network solutions, with innovation at the core of everything we do. Huawei established its first overseas R&D office in Sweden in 2000, and since then, we've expanded rapidly. Today, over 400 R&D engineers are pushing boundaries in Stockholm, Gothenburg, and Lund, working on groundbreaking 6G wireless systems that shape tomorrow's technology.

Join us on our journey to develop the future of tech and be a part of a team that thrives on innovation, works hard to solve the industry's biggest challenges, and collaborates with top engineers worldwide. If you're ready to contribute to world-changing advancements, we want you on board!

About the Thesis

For whom? 2 students in the second-year of master study, 30hp each

Wireless communication systems are increasingly complex, dynamic, and data-rich. Traditional machine learning (ML) models often require extensive retraining when environments change-posing challenges for real-time adaptation. Deep In-Context Learning (ICL), a paradigm where models learn to perform tasks by conditioning on examples without explicit parameter updates, offers a promising alternative. Recent breakthroughs in transformer-based architectures have shown that large models can generalize across tasks simply by observing input-output pairs.

This thesis project will develop a transformer-based ICL framework tailored for wireless communication involves channel estimation, signal detection, resource allocation, and modulation classification. The objectives include comparing transformer-based ICL = with conventional and supervise-learning based benchmarks; evaluating adaptability to channel conditions, signal-to-noise (SNR) ratios, and quadrature amplitude modulation (QAM) orders; and exploring few-shot and zero-shot learning capabilities. It aims to shed some light on answering the core question: Can ICL outperform or complement traditional and learning-based approaches in dynamic wireless environments without retraining-revolutionizing the transceiver system. It will also provide potential roadmap for integrating ICL into future 6G and beyond systems.

Timeline
We are offering both Master Thesis projects and internships. Opportunities are available starting now, with availability from spring 2026 also possible.

Your Profile

• Master students in the last-year of study-cycle
• Excellent scores in courses
• Good knowledges in AI, wireless communication, statistical signal processing, OFDM system,
• Experiences with Matlab, Pytorch, TensorFlow or other script languages and tools

Are you ready to apply your skills in a real-world setting and help us lead the charge in tech innovation? Join Huawei Sweden R&D, and let's make tech history together!

For more information regarding this opportunity, please contact:

Jonathan Lindberg, jonathan.lindberg@h-partners.com

Department Master Thesis Locations Lund