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Master Thesis: AI Edge in RAN CS

Ericsson AB

Sammanfattning

This opportunity offers two Master's Thesis assignments focused on next-generation (6G) radio base stations in Stockholm, Sweden. Students will engage in hands-on work within a Radio Access Network development environment, collaborating with experienced engineers to build and validate prototypes. Candidates can choose between assignments related to evolved storage solutions or AI-driven traffic management, aligning with their interests.
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Det här erbjuder vi

Hands-on experience in a cutting-edge technology environment.Opportunity to work closely with experienced engineers.Possibility to contribute to innovative solutions in the telecommunications field.

Stockholm

Ansök senast: Öppet tillsvidare
Publicerad: 2026-09-27

Beskrivning

Join our Team

About this opportunity:

We offer two Master's Thesis assignments at the heart of next-generation (6G) radio base stations, where robust data storage, energy efficiency and applied AI meet real product challenges.
You will work hands-on in a real Radio Access Network development environment, close to experienced engineers, building and validating working prototypes on production-grade hardware. Pick the assignment that matches your interests - or discuss with us which one fits you best.

What you will do:

Assignment 1 - Evolved local and external storage
  • Prototype how to store new AI/ML and observability data on the local disk despite size and endurance (TBW) limits, including efficient export and re-import.
  • Investigate how external (remote) storage can be set up, exposed, protected and read back, and how it behaves across node events (upgrade, rollback, disk outage).
  • Propose an aligned storage API supporting deployment-specific needs (size, write speed, response time, availability).


Assignment 2 - AI engine for in-flight, traffic-driven actuation
  • Profile realistic daily traffic curves (peak vs. off-peak) on a standalone cellular node.
  • Build a lightweight Deep Reinforcement Learning agent (e.g. PPO) that reacts to traffic in real time.
  • Actuate resources in-flight - adjust container core pinning and CPU frequency (DVFS) at runtime without restarting the container.
  • Validate power savings while proving zero service disruption, aligned with Green 6G KPIs.


The skills you bring:

Assignment 1:
  • A systems language (C, C++, Go or Rust) and comfort in Linux.
  • Knowledge of storage systems, file systems or distributed/remote storage.

Assignment 2:
  • Python and an RL framework
  • Linux internals, containers, CPU pinning and DVFS / power management.

Additional skills:
  • Good English skills
  • Great collaboration and team-player


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.

What happens once you apply? Click Here to find all you need to know about what our typical hiring process looks like.Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity Employer. learn more.

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

Req ID: 791350

Ansök till tjänsten

Master Thesis: AI Edge in RAN CS

Denna arbetsplats har annonserats på Ericsson-tjänsten den 2026-09-27 och publicerades av Ericsson.
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