The project aims to build AI methods that design cyclic peptides optimizing binding affinity, permeability, stability, and synthetic feasibility to enable oral delivery and replace parenteral biologics
Job Summary
The project aims to build AI methods that design cyclic peptides optimizing binding affinity, permeability, stability, and synthetic feasibility to enable oral delivery and replace parenteral biologics.
The PhD student will be integrated between Stockholm University and AstraZeneca, benefiting from interdisciplinary collaboration, access to industrial datasets, and mentorship from leading experts.
The Molecular AI department at AstraZeneca is a leading team applying AI to molecular design, supported by a diverse and highly qualified staff.
Matching Summary
The project aims to build AI methods that design cyclic peptides optimizing binding affinity, permeability, stability, and synthetic feasibility to enable oral delivery and replace parenteral biologics.
Skills & Requirements
Must-have
Molecular AI for peptide design
Proficiency in Python programming
Experience with modern deep learning models
Oral bioavailability and permeability modeling
Collaborative research environment
Nice-to-have
Peptide or macrocycle modeling experience
Familiarity with protein language models
Exposure to molecular simulation and MD
Workflow automation and HPC environments
Collaborative software development
Key Requirements
Master's degree or equivalent in relevant fields
Excellent written and verbal communication skills
Highly collaborative mindset
Application submitted through AstraZeneca portal by April 19, 2026