Master thesis: Fine-Tuning Foundation Models for Energy-Efficient 5G Orchestration

Teknik, data och digitalt · Data, AI och analys · Datavetenskap · Maskininlärning · DevOps

I korthet

This Master thesis project focuses on adapting time series foundation models for energy-efficient 5G orchestration by fine-tuning them on cloud-native network function telemetry metrics. The goal is to develop accurate forecasts for proactive network management, reducing over-provisioning and energy waste.

Ansvarsområden

  • Investigate adaptation of time series foundation models (e.g., TimesFM, TTM) to cloud-native 5G NF telemetry.
  • Conduct data collection from lab and testbed environments.
  • Perform systematic evaluation of foundation models in zero-shot and few-shot settings.
  • Potentially implement parameter-efficient fine-tuning techniques (LoRA, adapters).
  • Benchmark foundation models against classical baselines on NF energy, resource usage, and/or traffic prediction.
  • Develop a fine-tuning methodology for adapting foundation models to cloud-native 5G NF telemetry metrics.
  • Analyze model generalization across NF types, prediction targets, and training data regimes.
  • Provide recommendations for integrating foundation model-based forecasting into energy-aware network orchestration pipelines.

Krav

  • Background in machine learning, computer science, data science, or a related field.
  • Strong understanding of deep learning and time series modelling.
  • Proficiency in Python.
  • Familiarity with modern ML frameworks (Hugging Face, GluonTS, or similar).
  • 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.

Önskade kvalifikationer

  • Experience with PyTorch is advantageous.
  • Interest in cloud-native systems (Kubernetes, microservices) and sustainable computing.

Förmåner

  • Outstanding opportunity to use your skills and imagination to push boundaries.
  • Work on building solutions never seen before to tough problems.
  • Be challenged in a supportive environment with diverse innovators.
  • Chance to craft what comes next.
  • Encouraging a diverse and inclusive organization is core to our values.

Färdigheter

PythonPyTorchKubernetesmicroservicesHugging FaceGluonTS
#5G#orchestration#foundation models#energy efficiency#time series forecasting#cloud-native#Kubernetes#machine learning#deep learning#Python
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Företag

Ericsson

Publicerade jobb

för 4 dagar sedan

Anställningstyp

Praktik

Arbetsform

På plats

Erfarenhetsnivå

Student

Platser

Stockholm, Sweden

Kvalifikation

Masterexamen

Sökande

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