Master Thesis: Network Digital Twin Fidelity and Synthetic Data Generation for Radio Networks
Technology, Data & Digital · Software & Web Development · Software Engineering · Data Science · Machine Learning
In short
This Master's thesis project in Stockholm, Sweden, focuses on exploring Network Digital Twins (NDTs) for radio networks. The student will investigate NDT fidelity and synthetic data generation for AI applications, gaining experience in simulation, machine learning, and real-world data analysis.
Responsibilities
- 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 findings in a Master's thesis.
Requirements
- Enrolled in a Master's programme 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.
Desired Qualifications
- 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.
Benefits
- Supervision from experienced researchers and engineers.
- Access to simulation tools and real network data.
- Collaborative environment where you can shape the thesis direction.
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