Master Thesis

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At Cytiva, one of Danaher's 15+ operating companies, our work saves lives—and we're all united by a shared commitment to innovate for tangible impact.

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At Cytiva you will be able to continuously improve yourself and us – working on challenges that truly matter with people that care for each other, our customers, and their patients. Take your next step to an altogether life-changing career.

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Background
Affinity chromatography is a powerful technique for the purification of biomolecules, relying on specific interactions between a target molecule and a ligand immobilized on a chromatography resin. The design of novel ligands with improved selectivity, stability, and binding efficiency is crucial for advancing purification technologies, especially in biopharmaceutical manufacturing.

Recent advances in computational chemistry and molecular modelling offer new opportunities to accelerate ligand development through in silico methods. By simulating molecular interactions and predicting binding affinities, computer-aided design can reduce experimental workload and guide the synthesis of promising ligand candidates.

This thesis project aims to explore and apply computational tools for the design of novel ligands suitable for affinity chromatography.

What You'll Do

• Review relevant literature on ligand-target interactions and computational design strategies.
• Use molecular modelling software (e.g., AI models, docking, molecular dynamics) to simulate interactions between candidate ligands and target biomolecules.
• Evaluate ligand performance based on predicted binding affinities, specificity, and physicochemical properties.
• Propose a set of promising ligand structures for potential experimental validation (outside the scope of the thesis).

The project will be designed to ensure that all data and results can be anonymized for public presentation, avoiding disclosure of proprietary targets or molecules.

Who You Are

• Master's student in Data Science or Engineering with experience in computational chemistry/biology, scientific data analysis and/or bioinformatics.
• Eligible for master thesis work between January 2026 – June 2026.
• Background in machine learning and structural biology is advantageous.
• Independent, structured, and curious about optimization of AI models for molecular design and multivariate analysis.
• Proficiency in python and/or R and git-based workflows.
• Experience in cloud-based computing with AWS is a plus.

Interview and selection will happen continuously. For questions regarding the role please contact Jon Kapla,

Join our winning team today. Together, we'll accelerate the real-life impact of tomorrow's science and technology. We partner with customers across the globe to help them solve their most complex challenges, architecting solutions that bring the power of science to life.

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