Are you interested in AI and pharmaceutical manufacturing? In this thesis project, you will develop and evaluate a knowledge-graph-based maintenance agent using real-world maintenance data.

About AstraZeneca:

AstraZeneca is a global, science-led, patient-centred biopharmaceutical company focusing on discovering, developing, and commercialising prescription medicines for some of the world’s most serious diseases. But we’re more than a global leading pharmaceutical company. At AstraZeneca, we're dedicated to being a Great Place to Work and empowering employees to push the boundaries of science and fuel their entrepreneurial spirit.

About the Opportunity:

As a Thesis Worker at AstraZeneca, you’ll find an environment that’s full of unique opportunities and exciting challenges. Here, you’ll have the opportunity to pursue your areas of interest whilst equally developing a broad skillset and knowledge base to get the best out of your experience. You’ll be working on meaningful projects to make an impact and deliver real value for our patients and our business.

Thesis work description:

Sustaining high uptime in pharmaceutical production depends on how quickly the right knowledge reaches the person standing at the machine. This knowledge comes from two complementary sources: the data generated by production and the accumulated understanding of how equipment behaves and how faults have been resolved in the past. A reliable agentic system needs access to both.

In this thesis project, you will focus on the second source: transforming historical maintenance knowledge into a knowledge-graph-based maintenance knowledge base that can be queried by an agent powered by a large language model (LLM). The data consists of historical maintenance records written as free text by technicians who carried out the work

The project builds on an existing information-extraction pipeline and agent prototype. You will investigate how maintenance records can best be transformed into a useful and scalable knowledge graph, and evaluate how effectively the LLM-driven agent can retrieve and use this knowledge when answering realistic maintenance questions.

Key objectives:

• Verify the current knowledge graph construction pipeline and improve its underlying ontology against real maintenance records.
• Investigate strategies for building an inductive knowledge graph, one that takes in new work orders, equipment, and fault types without being rebuilt.
• Gather realistic maintenance questions from engineers and technicians and build them into an expert-reviewed evaluation set.
• You will work with real manufacturing data in a regulated environment and collaborate closely with operators, maintenance personnel and technical experts. The project combines practical work in agentic AI and data engineering with knowledge representation and evaluation in an industrial setting.

Outcome:

You will gain hands-on experience with agentic AI built on real manufacturing data in a regulated environment and will deliver a working agent together with an evidence-based assessment of its reliability.

Placement:  

This is an on-site position at AstraZeneca Södertälje.

Please note, AstraZeneca does not support with accommodations for this role.

Structure:

• Duration: Spring 2027
• Credits: 30
• You will work together with another master’s thesis student who has already been identified for the project.

Essential Requirements:

• Enrolled in a master’s programme in industrial engineering, computer science, artificial intelligence, production systems or a related field.
• Strong interest in LLM-Ops, agent development and applied artificial intelligence.
• Strong communication and collaboration skills, including the ability to interact with operators, engineers and subject-matter experts.
• Experience with knowledge graphs or information retrieval is an advantage but not a requirement.

So, what’s next?

Apply today and take the chance to be part of making a difference, making connections, and gaining the tools and experience to open doors and fulfil your potential. We can´t wait to hear from you!

We welcome your application as soon as possible, but ahead of the scheduled closing date October 13, 2026. In the event that we identify suitable candidates ahead of the scheduled closing date, we reserve the right to withdraw the vacancy earlier than published.

Date Posted

Closing Date