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This position is funded by DDLS (Data\u2011Driven Life Science / SciLifeLab) and is a collaboration with AstraZeneca, Molecular AI department, Discovery Sciences, Gothenburg, Sweden, and The Department of Molecular Biosciences, The Wenner-Gren Institute (MBW), Stockholm University. The project: many therapeutic proteins remain undruggable with small molecules, and convenient oral therapies are often lacking for chronic diseases. Cyclic peptides can bridge this gap by combining the tunability of small molecules with the specificity of biologics, and their stability and permeability can be enhanced using non\u2011natural amino acids. The overarching goal is to build AI methods that design cyclic peptides which co\u2011optimize binding affinity, permeability, stability, and synthetic feasibility\u2014ultimately enabling oral delivery and replacing parenteral biologics in selected indications. The project will build an end\u2011to\u2011end AI pipeline to design orally bioavailable cyclic peptides with non\u2011natural amino acids by uniting binder generation, property prediction, and experimental translation. We will extend sequence\u2011first joint structure-sequence design (EvoBindRare/RareFold) to broaden the nnAA library and programmatically exploit nnAA features while keeping synthesis feasible. In parallel, we will curate high\u2011quality datasets and develop models for permeability, stability, and aggregation using advanced descriptors and uncertainty\u2011aware scoring to form a unified oral bioavailability metric. This project provides comprehensive training in modern molecular generative AI, computational peptide design, property prediction, and industrial translation, aligned with DDLS goals. Focus areas of the position: molecular AI for peptide design, non\u2011natural amino acids and macrocyclic scaffolds, oral bioavailability, permeability, stability, and aggregation modeling. Essential skills and experience: Applications from highly motivated candidates are welcome. The successful candidate should have a Master's degree (or equivalent) in machine learning, computer science, computational chemistry, bioinformatics, applied mathematics, physics or related. Proficiency in Python and modern deep learning (e.g., transformers, diffusion, GNNs, generative models) applied to scientific problems as well as excellent written and verbal communication skills and highly collaborative mindset are also required. Desirable skills and experience: Experience with peptide or macrocycle modeling, molecular property prediction, amino\u2011acid/nnAA descriptors and QSAR. Familiarity with protein/peptide design, protein language models, and multi\u2011objective optimization in generative workflows. Exposure to molecular simulation/MD, 3D descriptor generation, workflow automation, version control and HPC environments. Collaborative software development experience The research group: The PhD student's time will be split between Stockholm University and AstraZeneca, Gothenburg, and the student will be integrated in both environments. At Stockholm University, the student will join the MBW department and collaborate closely with the academic PI, Dr. Patrick Bryant (DDLS Fellow), whose group develops advanced AI for peptide and protein design (e.g., RareFold and EvoBindRare). At AstraZeneca, the student will be embedded in the Molecular AI department and co\u2011supervised by Dr. Alessandro Tibo and Dr. Mikhail Kabeshov, with additional mentorship from Professor Ola Engkvist (Head of Molecular AI). The student will benefit from a vibrant interdisciplinary setting, regular joint project meetings, and access to industrial datasets and expertise across peptide chemistry, DMPK, and AI. AstraZeneca is a global, science-driven biopharmaceutical company. We are dedicated to turn ideas into life changing medicines and strive to continuously meet the unmet needs of patients worldwide. At AstraZeneca R&D in Gothenburg we have more than 3,000 people from over 50 countries to support generation of new medicines through drug discovery, development, and clinical trials. The Molecular AI department is leading in applying AI to molecular design including molecular generative AI, synthetic route prediction and molecular property prediction. The department consists of around 20 permanent staff scientists of which 90% have PhDs, 3-5 postdocs, 10-15 industrial PhD students funded from Horizon Europe, WASP and SSF, graduate students from the internal Data Science and AI graduate program and Master Thesis students. More information at: https://www.astrazeneca.com/ Stockholm University: the Department of Molecular Biosciences (MBW) at Stockholm University offers a dynamic, international research environment dedicated to answering fundamental questions in molecular and cellular biology. Located at the Arrhenius Laboratories on the Frescati Campus, MBW comprises 28 research groups and over 180 staff, including 47 PhD students, creating a collaborative setting for cutting-edge biological research. MBW emphasizes experimental approaches to explore gene and cellular functions in tissues and organisms, which supports a solid foundation for developing rigorous scientific skills. With well-established collaborations with healthcare organizations and industry, MBW also fosters translational research initiatives, allowing PhD students to bridge basic and applied research and gain experience with real-world applications. PhD students at MBW benefit from access to state-of-the-art facilities and an interdisciplinary network with neighboring departments in the natural sciences. The department's vibrant intellectual exchange, along with opportunities for interdisciplinary courses and active participation in Stockholm University's scientific community, ensures an enriching environment for advanced research and professional development. More information at https://www.su.se/english/divisions/department-of-molecular-biosciences-the-wenner-gren-institute How to apply: The application, written in English, is submitted through the application portal of AstraZeneca by April 19th, 2026 and should contain: Cover letter in which the applicant motivates their interest for the position and states relevant qualifications. CV including at least two references (phone number and email) Copies of relevant certificates, degrees and grades. Pay: According to local agreement Start date: 2026-09-01 or otherwise agreed. 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