Energy-Efficient 5G Orchestration with Foundation Models

Ericsson; Stockholms kommun; On-site; SEK 167,000 - 234,000; Full time

Job summary

Ericsson in Sweden invites you to join as a thesis student to explore cloud-native 5G NF forecasting, focusing on adapting time series foundation models to telemetry data for proactive energy-aware orchestration.

You will collect data from lab environments, evaluate models in zero-/few-shot setups, and potentially apply LoRA/adapters. Strong Python skills and familiarity with PyTorch and Hugging Face are valued, with international collaboration in Stockholm-based research.

Qualifications

• Background in machine learning, computer science, or data science.
• 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.

Responsibilities

• Investigate adaptation of time series foundation models to cloud-native NF telemetry metrics.
• Collect data from lab and testbed environments; evaluate models in zero-shot and few-shot settings.
• Explore parameter-efficient fine-tuning techniques (LoRA, adapters).
• Provide recommendations for integrating foundation model forecasting into energy-aware orchestration pipelines.

Skills

ML background

Deep learning

Time series modelling

PyTorch

Python

ML frameworks

English proficiency

Tools

Hugging Face

GluonTS

Job description

Ericsson in Sweden invites you to join as a thesis student to explore cloud-native 5G NF forecasting, focusing on adapting time series foundation models to telemetry data for proactive energy-aware orchestration.

You will collect data from lab environments, evaluate models in zero-/few-shot setups, and potentially apply LoRA/adapters. Strong Python skills and familiarity with PyTorch and Hugging Face are valued, with international collaboration in Stockholm-based research.