Master's Thesis: Learning Progressively Smaller Models for Mobile Networks via Self-Distillation

Technology, Data & Digital · Data, AI & Analytics · Machine Learning · Data Science · Software Engineering

In short

This Master's thesis focuses on developing a self-distillation framework to create smaller, more efficient AI models for mobile networks, aiming to maintain accuracy while reducing computational costs. The project involves research, implementation, and evaluation of these models using real-world datasets, with a preferred start in Spring 2027 at Ericsson Research in Stockholm.

Responsibilities

Review research on tabular foundation models, continual learning, and knowledge distillation for mobile networks.
Design a self-distillation pipeline that adapts to incoming data and tasks under realistic mobile-network conditions.
Implement the framework and evaluate it on real-world datasets with evolving data distributions and new task scenarios.
Analyze trade-offs among prediction accuracy, adaptation speed, computational cost, model size, and interpretability.
Present findings in a written thesis and at Ericsson Research.
Develop own ideas at the intersection of foundation models, continual learning, and efficient AI for future mobile networks.

Requirements

Pursuing a master’s degree in Computer Science, Artificial Intelligence, Data Science, Electrical or Computer Engineering, or a related field.
Strong programming skills in Python and PyTorch.
Knowledge of AI; a background in machine learning and neural networks is preferred.
Fluency in English.

Desired Qualifications

Knowledge of model compression, distillation, or tabular foundation models is an advantage.
Interest in AI research or building end-to-end AI prototypes.
Familiarity with Git is a plus.

Benefits

One student, 30 ECTS credits.
Opportunity to develop own ideas.
Work at Ericsson Research.

Skills

PythonPyTorchmachine learningneural networksmodel compressionknowledge distillationtabular foundation modelsGit

#Knowledge distillation#tabular foundation models

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