Master Thesis: Network Digital Twin Fidelity and Synthetic Data Generation for Radio Networks
About this opportunity:
We are looking for a Master's student interested in wireless communications, simulation, and machine learning. The thesis will explore how Network Digital Twins (NDTs) can accurately represent real radio networks and efficiently generate synthetic data for AI and analytics applications.
A Network Digital Twin is a simulation-based virtual replica of a radio network used to predict performance, evaluate new features, and support network optimization. Its usefulness depends on:
Fidelity: how accurately it reproduces real network behaviour.
Efficiency: how effectively it generates data for machine learning and analytics.
The scope will be adapted to your interests and background. Possible focus areas include:
Evaluating Digital Twin fidelity by comparing simulated KPIs with real network measurements and investigating calibration methods.
Exploring synthetic data generation for machine learning, including the trade-off between simulation fidelity, cost, and model performance.
Relevant metrics may include RSRP, SINR, throughput, mobility performance, and other radio network KPIs.
You will gain experience in Network Digital Twin modelling and validation, radio network simulation, real-world measurement analysis, machine learning for wireless systems, experimental research, and large-scale datasets. We offer supervision from experienced researchers and engineers, access to simulation tools and real network data, and a collaborative environment in which you can shape the thesis direction.
What you will do:
Depending on the selected focus area, you will:
Configure and run system-level radio network simulations.
Analyse and compare simulation results with real network measurements.
Define and evaluate simulation-fidelity metrics.
Investigate mismatches between simulated and real-world behaviour.
Explore calibration and modelling improvements.
Design and evaluate synthetic data generation approaches.
Train and evaluate machine learning models using synthetic, real, or mixed datasets.
Document and communicate your findings in a Master's thesis.
The skills you bring:
You are enrolled in a Master's programme in Electrical Engineering, Computer Science, Engineering Physics, Data Science, or a related field.
You have programming experience in Python.
You have a background in wireless communications, machine learning, statistics, or another quantitative discipline.
You are interested in simulation, data analysis, and research.
You have strong analytical, problem-solving, written, and verbal communication skills.
The following are considered a plus:
Familiarity with wireless communication systems, including 5G NR.
Experience with simulation tools or modelling environments.
Knowledge of machine learning workflows and data analysis.
Experience with experimental or measurement data.
Familiarity with reproducible research or software development practices.
Skills Required
- Enrollment in a Master's program in Electrical Engineering, Computer Science, Engineering Physics, Data Science, or a related field
- Programming experience in Python
- Background in wireless communications, machine learning, statistics, or another quantitative discipline
- Interest in simulation, data analysis, and research
- Strong analytical, problem-solving, written, and verbal communication skills
- Familiarity with wireless communication systems, including 5G NR
- Experience with simulation tools or modeling environments
- Knowledge of machine learning workflows and data analysis
- Experience with experimental or measurement data
- Familiarity with reproducible research or software development practices
What the Team is Saying
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What We Do
Ericsson builds the digital connectivity the world relies on. Our technology underpins the mobile networks, platforms, and systems that billions of people, businesses, and societies depend on every day. We are a global leader in communications technology, delivering mobile network infrastructure, cloud software, and wireless connectivity solutions for service providers and enterprises worldwide. Our networks support connectivity across 180+ countries, helping power everyday communication as well as critical digital services at global scale. Connectivity has evolved far beyond consumer mobile use. Today, nearly 80% of the world’s population accesses the internet via mobile networks, and Ericsson is helping shape what comes next. We are advancing 5G and 5G Advanced, developing network APIs that open connectivity to the global developer ecosystem, and applying automation and AI to make networks more intelligent, efficient, and resilient. Ericsson was the first company to launch live 5G networks on five continents, and our 5G platform is now commercially live in 150+ networks across 60+ countries. We also support more than 36,000 enterprise customers, enabling secure, high-performance connectivity for industries such as manufacturing, aviation, logistics, utilities, and public safety, where reliability and performance are mission critical. Innovation is central to how we work. Ericsson has approximately 28,000 employees in research and development, backed by one of the strongest intellectual property portfolios in the industry with 60,000+ granted patents. Our engineers, researchers, and technologists work across 100+ global R&D sites, helping define how networks evolve and how digital infrastructure is built for the long term. As the world moves toward a mobile-first, AI-powered, and cloud-driven future, connectivity becomes the foundation for digital transformation across every industry. Ericsson is building that foundation, shaping the future of digital connectivity through technology that operates at global scale and supports real-world impact, today and for what comes next.
Why Work With Us
Ericsson is a place for people who want to work on technology that powers everyday life. You’ll contribute to large-scale systems used every day, tackle complex challenges in live environments, and keep developing your skills and career in your own vision.
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