Master Thesis: AI-Assisted Integrated Data Center Design for Telecom Core Systems

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Telecom core solutions combine multiple products and cloud-native network functions. Product-level dimensioning provides inputs such as compute, memory, storage, replica and workload requirements. These outputs, together with customer requirements and engineering constraints, form the basis for the subsequent integrated Data Center design.

Integrated Data Center design is not limited to calculating or valuing hardware. It is the process of deciding how the complete solution should be structured and deployed: how many Data Centers, PODs, vPODs, clusters and node pools are needed; which functions should be placed together or separated; how storage, networking, racks, zones, failure domains and spare capacity should be configured; and how the design should support resilience, lifecycle management, operations and future expansion.

We are looking for 1-2 students for this thesis project, based either in Kista or Lund.

This thesis will investigate how AI-assisted decision support can improve a representative integrated Data Center design process for core solutions. The student will combine customer needs, engineering rules and deterministic models to generate, validate and explain a complete design.

Mapping the design process from customer requirements and product dimensioning; identifying the major design decisions and their dependencies across Data Centers, PODs, vPODs, clusters, node pools, CNF placement, storage, networking, racks, zones and failure domains.Developing a prototype that helps organise requirements, identify missing or conflicting information, and propose alternative designs; validating alternatives with deterministic checks for capacity, high availability, placement, compatibility, upgrades, resilience and physical infrastructure.Comparing designs using relevant criteria such as footprint, resilience, operational complexity, lifecycle impact, scalability, cost and expansion flexibility; producing traceable design documentation.Evaluating which activities can be automated and where human judgement remains necessary.Grünig and Kühn’s complex decision-making framework will provide the methodological backbone, while an agentic AI runtime will support the end-to-end process by coordinating specialised agents and maintaining the evolving problem state and traceability.

The thesis is suitable for a student with an interest in, and preferably some experience with:

Telecom core networksCloud-native infrastructureData center designAI-assisted engineeringDecision makingExpert systemsAgentic delegationComplex problem solving.

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.