Master Thesis - Fine - Tuning Foundation Models - Energy - Efficient 5G Orchestration

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About this opportunity:

Cloud-native 5G deployments run Network Functions (NFs) as Kubernetes-managed microservices on shared infrastructure, and as RAN components and core NFs migrate to centralised cloud environments, their energy consumption, resource utilisation, and traffic patterns become critical inputs to orchestration decisions such as scaling, scheduling, and workload consolidation. Current orchestration controllers act reactively - after load changes have already occurred - leading to over-provisioning and energy waste. Accurate short-to-medium horizon forecasts of energy usage, CPU/memory utilisation, and traffic volume at the NF and pod level are therefore essential for proactive, energy-aware management of cloud-native 5G networks.

What you will do:

This thesis will investigate the adaptation of time series foundation models (e.g., TimesFM, TTM) to cloud-native 5G NF telemetry metrics through domain-specific fine-tuning, with the goal of producing accurate and calibrated forecasts that can serve as inputs to energy-efficient orchestration systems. The work will encompass data collection from lab and testbed environments, systematic evaluation of foundation models in zero-shot and few-shot settings, and potentially parameter-efficient fine-tuning techniques (LoRA, adapters). The expected outcome may include:

• a benchmarking study comparing time series foundation models against classical baselines on NF energy, resource usage, and/or traffic prediction;
• a fine-tuning methodology for adapting foundation models to cloud-native 5G NF telemetry metrics;
• an analysis of model generalisation across NF types, prediction targets, and training data regimes; and
• recommendations for integrating foundation model-based forecasting into energy-aware network orchestration pipelines.

The skills you bring:

• Background in machine learning, computer science, data science, or a related field.
• Strong understanding of deep learning and time series modelling; experience with PyTorch is advantageous.
• Proficiency in Python and familiarity with modern ML frameworks (Hugging Face, GluonTS, or similar).
• Interest in cloud-native systems (Kubernetes, microservices) and sustainable computing.
• Ability to conduct technical literature reviews, design experiments, analyse results, and document findings.
• Strong analytical and problem-solving skills.
• Ability to work independently while communicating effectively in an international research environment.
• Good written and spoken English.

Why join Ericsson?At Ericsson, you´ll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what´s possible. To build solutions never seen before to some of the world's toughest problems. You´ll be challenged, but you won't be alone. You´ll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next.

Primary country and city: Sweden (SE) || Stockholm

Req ID: 791022