Thesis work, 30 credits - Advance AI for Drug Discovery with MolecularAI @ AstraZeneca

Are you interested in shaping the future of drug discovery using the latest in artificial intelligence and machine learning? Join the MolecularAI group at AstraZeneca, where we use state-of-the-art algorithms, molecular simulation, and data science to accelerate innovation in medicine.  

Our tools—including the open-source platforms ReInvent for molecular design, AiZynthFinder for synthesis planning, and Maize for advanced simulation pipelines—are widely adopted in real-world drug discovery projects. 

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: 

We're offering thesis opportunities to motivated students who want to help push the boundaries of our models and address the next generation of challenges in computational chemistry and machine learning. Work alongside leading experts, and tailor your project to match your interests and strengths.  

At AstraZeneca, you'll find a collaborative environment that values creativity, open science, and hands-on learning. Our projects can be customized to suit your academic background and research goals, whether you are passionate about deep learning, chemistry, data science, or scientific software development. 

Potential Research Areas: 

• Agentic AI Architectures: develop next-gen autonomous systems for molecular design . 
• Chemical Reasoning with Large Language Models (LLMs): Enable AI systems to interpret and reason about chemical problems 
• Flow Matching and Diffusion Models for Atomic Point Clouds: Explore advanced generative frameworks for molecular representations. 
• Exploring Novel Deep Learning Architectures: Drive research into cutting-edge model designs for complex chemical data. 
• Uncertainty and Multi-Objective Optimization: Improve decision-making in drug discovery by modeling uncertainty and balancing multiple goals. 
• Combining Machine Learning with Simulations for Molecular Property Prediction: Integrate predictive models with physical simulations of large biomolecules. 
• Ligand Binding Affinity Prediction with Machine-Learned Potentials: Innovate new predictors to assess binding and efficacy. 
• Investigating the Effect of Model Bias in Molecular Property Prediction: Analyze and mitigate systematic errors in ML models for chemistry. 

Structure:

• Duration: Spring term 2026 
• Credits: 30 

If the university does not approve individual thesis projects, we ask that 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 name of your partner. 

Essential Requirements: 

• Enrolled in a Master's program focused in computer science, machine learning, AI, complex adaptive systems, or similar 
• Knowledge in python programming, AI, machine learning, or algorithms. 
• Chemistry background is not mandatory. 

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 30th of September 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

09-sep.-2025

Closing Date

30-sep.-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.