Thesis work, 30 credits - AI Surrogates from 3D CFD for Fast Prediction and Accelerated Process Development

Are you interested in accelerating drug development through advanced simulation techniques? This thesis offers the opportunity to build AI models that predict key hydrodynamic and mixing metrics at a fraction of the runtime compared to traditional 3D mechanistic simulations. The project aims to enable efficient exploration and optimization of process design spaces by delivering robust, high-accuracy surrogate models. 

About AstraZeneca: 

AstraZeneca is a global, science-led, patient-centered biopharmaceutical company focusing on discovering, developing, and commercializing 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:   

In this project, you will collect simulation data across various geometries, boundary conditions, and operating ranges to train AI models—including reduced order models (ROMs) and 3D AI models capable of field-to-field mappings. These models will approximate full flow fields while preserving spatial structure, followed by rigorous cross-validation and uncertainty quantification to assess predictive robustness across both interpolation and extrapolation domains. 

Key Deliverables: 

• Develop and benchmark AI surrogates from 3D CFD models of mixing tanks 
• Create a reproducible end‑to‑end workflow for surrogate model development, clearly specifying data and tooling requirements, model and domain assumptions, and known limitations/failure modes 
• Write a report summarizing the data generation and modeling approach, model credibility assessment including validation and verification outcomes. 

Placement: 

This is an on-site position at AstraZeneca Gothenburg. AstraZeneca does not support with accommodations for this role.  

Structure: 

Duration: Fall 2026 

Credits: 30 

Essential Requirements: 

• Enrolled in a Swedish’s University 
• Enrolled in a Master's program within a relevant field. 
• Some prior knowledge of transport phenomena, AI/ML, and CFD is preferred but not compulsory.  

You will be trained on the necessary software packages and complementary skills required for this project.  

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 15th of April 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

25-Mar-2026

Closing Date

15-Apr-2026

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.