Thesis work, 30 credits - Improving Machine Uptime in Pharmaceutical Manufacturing Using AI-Driven Troubleshooting Tools

Are you eager to shape the future of pharmaceutical production through digital innovation? Do you enjoy problem-solving and want to see your ideas drive real improvements on the factory floor? We invite you to join us in developing and testing an advanced Maintenance agent that streamlines machine troubleshooting for operators. 

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: 

Background: Unplanned downtime is a significant challenge in pharmaceutical manufacturing, often resulting in long waiting times for technical specialists. To overcome this, we have developed a prototype Maintenance agent powered by a generative pre-trained transformer (GPT) and a curated knowledge base of approximately 1,000 common fault–fix pairs. This AI-based agent is designed to guide machine operators step by step, enabling them to resolve frequent issues independently and reduce reliance on expert intervention. 

Thesis focus: As a thesis worker, you will analyze historical downtime data to estimate how many events can be solved using the agent, optimize the underlying data pipeline, and validate the bot’s effectiveness by testing it on real-world fault scenarios—with expert input. Furthermore, you will quantify the reduction in waiting times and evaluate the impact on overall equipment effectiveness (OEE). 

Objectives: 

• Analyze historical downtime data to estimate the share of events solvable with the agent. 
• Optimize the data pipeline 
• Test the agent on real fault scenarios with expert-validated outcomes. 
• Quantify potential reductions in waiting time and impact on OEE. 

This project is ideal for students with a background in engineering, automation, data science, or computer science, and an interest in industrial digitization and AI applications. You will develop your analytical and practical skills through direct work with production data, digital process tools, and collaborative testing in a real manufacturing environment. 

Structure: 

• Duration: Spring term 2026 
• Credits: 30 
• The Thesis can be performed by one or two students 

If you apply together with a partner, each person must submit their own application separately, but you should mention in your personal letter that you are applying jointly and include the full name of your partner. 

Essential Requirements: 

• Enrolled in a master's program focused on engineering, automation, data science, or computer science.  

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 26th of October 2025. 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

13-okt.-2025

Closing Date

26-okt.-2025

Our mission is to build an inclusive and equitable environment. We want people to feel they belong at AstraZeneca and Alexion, starting with our recruitment process. We welcome and consider applications from all qualified candidates, regardless of characteristics. We offer reasonable adjustments/accommodations to help all candidates to perform at their best. If you have a need for any adjustments/accommodations, please complete the section in the application form.

AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorisation and employment eligibility verification requirements.