Thesis Work, 30/60 Credits - Advancing AI-Driven Mechanism-of-Action Prediction from Cell Painting Images: Expanding the DeepPheno Platform

Are you passionate about applying artificial intelligence to accelerate drug discovery? Join AstraZeneca’s Biosciences Technology Imaging Team for a master’s thesis project focused on further developing DeepPheno, an AI platform that predicts the mechanisms of action of compounds from Cell Painting images.

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:

AstraZeneca is looking for a motivated master’s student to continue developing DeepPheno, an artificial intelligence platform that predicts the mechanism of action of compounds from Cell Painting images.

Cell Painting captures how compounds change cell morphology and provides valuable insights for early drug discovery. Previous work has shown that these images contain meaningful mechanism-of-action signals, while also highlighting opportunities to improve prediction accuracy, biological interpretation, and practical usability.

Key Objectives:

• Improve prediction performance by evaluating modern artificial intelligence models and larger Cell Painting datasets.
• Integrate complementary biological data, such as gene-expression profiles, chemical structures, target annotations, and pathway information.
• Improve model explainability by identifying the cellular features, image regions, and fluorescence channels that influence predictions.
• Evaluate the platform for drug discovery by assessing prediction confidence, model calibration, and performance on previously unseen compounds.
• Contribute to the next generation of DeepPheno by improving its usability, flexibility, and documentation.
• The project may involve image analysis, machine learning, deep learning, biological data integration, software development, and visualization using Python. The exact focus will be adapted to the student’s interests and background.
• The thesis can be completed for either 30 or 60 credits, depending on the requirements of the student’s university. The project scope, objectives, and expected deliverables will be adapted to align with the selected credit level and the student’s academic programme.

Placement:

This is an on-site position at AstraZeneca Gothenburg.

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

Structure:

• ·Duration: Start Jan 2027
• Credits: 30/60

Essential Requirements:

• Enrolled in an MSc programme in Bioinformatics, Data Science, Artificial Intelligence, Computer Science, Biomedical Engineering, Biotechnology, or a related field
• Strong Python programming skills and experience with deep learning frameworks (PyTorch preferred)
• Interest in drug discovery, microscopy, image analysis, and AI for life sciences.
• Ability to work independently, analyze results, and communicate findings clearly to both scientific and non-specialist audiences.
• Basic knowledge of machine learning, deep learning, or image analysis.

Desirable Requirements:

• Experience with computer vision, self-supervised learning, or multimodal AI.
• Experience working with biological data or microscopy.

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 04, 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