Industrial PhD student in Molecular AI

Project: AI for Orally Bioavailable Cyclic Peptide Design (NonNatural Amino Acids)

We are now looking for an Industrial PhD student in Molecular AI to develop nextgeneration AI that designs potent orally bioavailable cyclic peptides with nonnatural amino acids and help unlock undruggable targets. This position is funded by DDLS (DataDriven 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 remainundruggablewith small molecules and convenientoral therapiesare often lacking for chronic diseases.Cyclic peptidescan bridge this gap by combining the tunability of small molecules with the specificity of biologics and their stability and permeability can be enhanced usingnonnatural amino acids. The overarching goal is to build AI methods that design cyclic peptides which cooptimizebinding affinitypermeabilitystability andsynthetic feasibilityultimately enabling oral delivery and replacing parenteral biologics in selected indications. The project will build an endtoend AI pipeline to design orally bioavailable cyclic peptides with nonnatural amino acids by uniting binder generation property prediction and experimental translation. We will extend sequencefirst joint structuresequence design (EvoBindRare/RareFold) to broaden the nnAA library and programmatically exploit nnAA features while keeping synthesis parallel we will curate highquality datasets and develop models for permeability stability and aggregation using advanced descriptors and uncertaintyaware scoring to form a unified oral bioavailability metric. This project provides comprehensive training in modernmolecular generative AIcomputational peptide designproperty prediction andindustrial translation aligned with DDLS goals.

Focus areas of the position: molecular AI for peptide design nonnatural 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 Masters 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 aminoacid/nnAA descriptors and QSAR.

• Familiarity with protein/peptide design protein language models and multiobjective 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 students 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 cosupervised 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 3000 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: 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 departments vibrant intellectual exchange along with opportunities for interdisciplinary courses and active participation in Stockholm Universitys scientific community ensures an enriching environment for advanced research and professional development.

More information at 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: or otherwise agreed.

Type of employment: Temporary position 4 years

Working hours: 100%

Date Posted

Closing Date