Job Description

Join our Team

About this opportunity:

Ericsson Research’s Artificial Intelligence Research Area pushes the frontiers of AI by combining machine learning and reasoning methods to enable intelligent, autonomous operations in large and complex telecom systems.

We are looking for a talented and motivated student to join a study on Federated Telecom Foundation Models.

Foundation models are becoming key building blocks of the AI-native network. They can generalize across management, optimization, and automation tasks, but developing and deploying them at scale requires extensive data and computational resources. Telecom data is often geographically distributed, continuously evolving, and subject to privacy, ownership, and regulatory constraints. In addition, models with billions of parameters are costly to train and maintain, both computationally and in terms of communication.

Federated Learning offers a promising solution by enabling multiple parties to train foundation models collaboratively without sharing their underlying data. However, important research challenges remain, including handling large and sparse models efficiently, reducing communication overhead, protecting intellectual property, enabling continuous model adaptation, and developing sustainable incentive mechanisms for collaborative AI ecosystems.

What you will do:

• Review research on foundation models, federated learning, and telecom applications.
• Evaluate existing approaches and establish a baseline.
• Extend state-of-the-art methods and develop novel techniques for collaborative training and model adaptation.
• Define research directions together with your supervisor.
• Collaborate with the research team to ensure technical feasibility.
• Present your findings through regular discussions and final thesis documentation.

The skills you bring:

• What We Offer
• The project will be conducted at Ericsson Research in Kista during Spring 2027. You will receive mentorship from senior researchers, access to industry tools and datasets, and the opportunity to support Ericsson’s work towards an AI-native network. Outstanding results may contribute to scientific publications, patent applications, and future Ericsson research activities.
• Master’s student with most coursework completed and strong academic performance.
• Proficiency in Python and hands-on experience with machine learning frameworks such as PyTorch.
• Knowledge of distributed systems, federated learning, large language models, or foundation models is beneficial.
• Strong programming, debugging, analytical, and problem-solving skills, including effective use of generative AI development tools.
• Excellent written and spoken English and the ability to work effectively in an international team.
• Knowledge of telecommunications networks is preferred.
• An independent, curious, and research-oriented mindset, with the ability to learn quickly and identify problems and solutions.