Are you excited by the challenge of advancing siRNA therapeutics for precision medicine? Join our collaborative teams in Advanced Drug Delivery and Predictive Science for a Master’s thesis focused on developing modelling-enabled strategies to optimize high-concentration siRNA formulations. Gain hands-on experience with formulation development, high-throughput viscosity screening, NMR analysis, and data modelling—helping accelerate the delivery of innovative siRNA treatments to patients. 

About AstraZeneca: 

AstraZeneca is a global, science-led, patient-centred biopharmaceutical company focusing on discovering, developing, and commercialising prescription medicines for some of the world’s most serious diseases. But we’re more than a global leading pharmaceutical company. At AstraZeneca, we're dedicated to being a Great Place to Work and empowering employees to push the boundaries of science and fuel their entrepreneurial spirit. 

About the Opportunity:

As a Thesis Worker at AstraZeneca, you’ll find an environment that’s full of unique opportunities and exciting challenges. Here, you’ll have the opportunity to pursue your areas of interest whilst equally developing a broad skillset and knowledge base to get the best out of your experience. You’ll be working on meaningful projects to make an impact and deliver real value for our patients and our business.

Thesis work description: 

This project is a collaboration between the Oligonucleotide and Peptide Clinical Formulation Development team in Advanced Drug Delivery (ADD) and the modelling team in Predictive Science, Digital and Automation (PSDA), aimed at delivering a modelling-enabled framework that guides siRNA clinical formulation strategy to accelerate the delivery of siRNA therapeutics to patients. 

Small interfering RNA (siRNA) is a key modality for precision medicine due to its ability to selectively modulate gene expression, thus enabling the targeting of drug candidates previously considered undruggable with traditional small molecule therapeutics. 

Subcutaneous administration is the preferred route for siRNA therapeutics, but  the limited injection volume necessitates highly concentrated siRNA formulations. Achieving the desired therapeutic effect in clinical trials often requires high-concentration siRNA, which in turn leads to increased formulation viscosity—a major bottleneck in siRNA development for subcutaneous use. 

To address this, the project integrates high-throughput experimentation with the development of modelling tools to inform siRNA formulation design for clinical studies. Conjugation of siRNA with diverse ligands enables targeted tissue delivery, and initial experiments indicate that both siRNA sequence and ligand type significantly impact viscosity. Our strategy involves screening viscosity-modifying excipients to reduce formulation viscosity. This requires scientific understanding of intermolecular siRNA and siRNA–excipient interactions at high concentrations.  For that NMR will be used to elucidate siRNA organization and interactions with excipients. 

Key Objectives 

• The student will familiarize themselves with relevant literature and receive training in siRNA formulation and analytical techniques for characterizing critical quality attributes. 
• High-throughput  viscosity measurements will be employed to screen and evaluate the viscosity reduction potential of selected excipients (at least two). 
• Excipients that reduce viscosity will be further studied using NMR analysis (at least one). 
• The student will support the development of models linking siRNA sequence/conjugation and solution conditions (buffer, pH, ionic strength) to key readouts (formulation  viscosity), leveraging insights from experimental and NMR data. 

Essential Requirements: 

• Enrolled in a Master’s program at a Swedish university 
• Strong interest in drug formulation, biopharmaceuticals, or related life science fields 
• Basic wet chemistry skills 
• Ability to work independently and collaboratively in a multidisciplinary team 
• Good analytical and problem-solving abilities 
• Effective communication skills in English (written and spoken) 
• Interest in data analysis and modelling tools 

Desirable Requirements: 

• Previous experience with running simulations or developing physics based/Machine Learning based models will be considered an advantage, but training will be provided 

Placement:   

This is an on-site position at AstraZeneca Gothenburg. 

AstraZeneca does not support with accommodations for this role. 

Structure: 

• Duration: August 2026 
• Credits: 30 

So, what’s next? 

Apply today and take the chance to be part of making a difference, making connections, and gaining the tools and experience to open doors and fulfil your potential. We can´t wait to hear from you! We welcome your application as soon as possible, but ahead of the scheduled closing date 30th April 2026. In the event that we identify suitable candidates ahead of the scheduled closing date, we reserve the right to withdraw the vacancy earlier than published. 

Date Posted

13-Apr-2026

Closing Date

30-Apr-2026

Our mission is to build an inclusive and equitable environment. We want people to feel they belong at AstraZeneca and Alexion, starting with our recruitment process. We welcome and consider applications from all qualified candidates, regardless of characteristics. We offer reasonable adjustments/accommodations to help all candidates to perform at their best. If you have a need for any adjustments/accommodations, please complete the section in the application form.