Thesis Work, 30/60 Credits – Confluency Atlas: Predicting Cell Growth and Cellular State Using AI

Role Overview

Join AstraZeneca to explore how cell growth dynamics may reveal deeper biological phenotypes, including cell cycle progression and cellular state. This thesis project combines cell biology, machine learning, FAIR data principles, and digital laboratory innovation to support future experimental planning and AI-enabled assay development.

What You Will Do

The Confluency Atlas is an AI-driven platform that transforms time-series cell growth data into predictive models for cell seeding and culture optimization. This thesis will investigate whether patterns in cell growth can be linked to deeper biological phenotypes, including cell cycle progression and cellular state.

Why It Might Be a Fit

The work will bring together cell biology and AI, applying FAIR data principles to support the effective use of experimental data. By connecting cell growth patterns with cellular phenotypes, the project will contribute to digital laboratory innovation and help inform future AI-enabled reagent and assay design, automation, acceleration, and experimental planning across Discovery Sciences.

Requirements

• Enrolled in a Master’s programme in cell biology or a related field.
• Good knowledge of cell biology.
• Interest in cell growth, cell culture, and cellular phenotypes.
• Familiarity with AI models or machine-learning models is an advantage
• Experience with hands-on cell culture is a plus.
• Experience with programming, such as Python or R, is an advantage.
• Good communication skills in English.