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

AstraZeneca is seeking a Master’s thesis student to develop AI surrogate models that replicate insights from complex 3D mechanistic simulations (e.g., CFD) at a fraction of the runtime. The goal is to enable efficient, robust design space exploration and optimization in drug development process by building AI models that predict key hydrodynamic and mixing metrics with high accuracy and substantial speedup. 

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:   

With in the master thesis the student will curate or generate CFD datasets that capture representative geometries, boundary conditions, and operating ranges, and use these data to train AI models that may include reduced order models (ROMs) and 3D AI models that learn field-to-field mappings to approximate full flow fields while preserving spatial structure. This will be followed by rigorous cross-validation across interpolation and extrapolation ranges, and uncertainty quantification to assess model robustness.  

Model evaluation will include cross-validation across interpolation and extrapolation domains and uncertainty quantification to characterize predictive reliability. Deliverables will comprise validated surrogate models for mixing applications; a performance summary detailing predictive quality, fidelity of the AI model, and computational cost analysis; deployment guidelines covering data specification and preparation, model interpretability, and reproducibility; and a decision framework specifying selection criteria for scalar/ROM surrogates versus 3D field-to-field models, with recommendations for drug development use cases and integration into end-to-end process modeling workflows. 

Structure: 

• Duration: Spring 2026 
• Credits: 30 

Essential Requirements: 

• 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 2nd of November 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

22-okt.-2025

Closing Date

02-nov.-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.