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About this opportunity

Large networks contain thousands of routers, switches and firewalls from different vendors, each with its own configuration language. Configuration management systems hide those differences: an operator makes a high-level request, and the system produces and sends the exact commands required by the target device. Templates make this translation possible, but writing and troubleshooting them requires knowledge of the template format, device command syntax and the high-level model being translated. Generative AI could assist with both tasks. The key challenge is determining whether its output is actually correct. In this domain, candidate templates can be checked automatically for format, translation and compilation, execution against a simulated device, and whether the produced commands are correct.

What you will do

This thesis investigates whether generative AI can write correct device configuration templates and explain and repair templates that do not work. You will first build an automatic judge that evaluates candidates through increasingly demanding checks. On top of this foundation, you will develop and evaluate AI pipelines for template authoring and troubleshooting. The two parts share the judge and experimental setup, making the project suitable for two students who can focus on complementary areas.

You will have freedom to define areas of exploration within the project. These could include, but are not limited to:

• Building a staged checker for template format, translation and compilation, execution against a simulated device, and command correctness.
• Generating templates from task descriptions, target-device documentation and existing examples.
• Comparing generated templates with expert-written templates and measuring how far candidates progress through the checks.
• Investigating which input information matters by removing one source at a time.
• Evaluating whether feedback-driven retries improve results and how many retries are worthwhile.
• Diagnosing failing templates, proposing fixes, and testing them against faults with known corrections.
• Categorising remaining failures and examining how well success in simulation predicts success on real devices.

The skills you bring

• Knowledge in Computer Science, Computer Engineering, or a related field.
• Experience with Scala or Java; Python or a similar scripting language is also relevant.
• Interest in generative AI applications and APIs, such as Amazon Q, Gemini or ChatGPT.
• Familiarity with XML and Docker/Kubernetes is beneficial.
• Interest in computer networks, developer tooling and empirical evaluation.
• Eagerness to learn and explore new concepts. No prior knowledge of network configuration protocols is required.

Extent: 2 students, 30 hp each
Location: Karlskrona
Preferred starting date: January 2027

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.