Are you passionate about using statistical modelling, machine learning and simulation to address complex challenges in clinical drug development? Join AstraZeneca in Gothenburg for a thesis project focused on developing digital twins to support the evaluation of treatment efficacy when placebo or standard-of-care comparisons are difficult or infeasible.

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 is an opportunity for an engaged master student to join our Clinical Pharmacology and Pharmacometrics team at AstraZeneca Gothenburg to work on an exciting project in the field of digital twins in Immunology.

Evaluation of clinical efficacy during drug development can be complex when comparison to placebo or standard of care is infeasible. In such settings, in silico treatment arms (herein referred to as digital twins) can provide a comparator for a more robust efficacy evaluation. In the present context, digital twins constitute a type of disease model used to generate potential disease trajectories for an individual patient under specified treatment scenarios. The generated disease trajectories can be compared to actual observed trajectories in individual patients thus allowing for, for example, drug versus placebo comparison. Different modelling approaches can be used to generate digital twins; this project will focus on data-driven approaches that learn patterns of disease progression from data.

The overall aim of the project is to develop a framework for the construction of digital twins to support efficacy evaluation during clinical drug development. Simulated data will primarily be used for method development, although real-world data may be used if relevant and accessible.

Key Objectives:

• Exploration of generative models, e.g. autoregressive models, to be used as digital twins.
• Development of a framework for comparison between observed clinical data and in silico counterfactuals to quantify efficacy.
• Validation and benchmarking of the framework.
• Setting up methodology for handling of missing data (stretch objective).

Placement:  

This is an on-site position at AstraZeneca Gothenburg.

Please note, AstraZeneca does not support with accommodations for this role.

Structure:

• Duration: Spring 2027
• Credits: 30/60

Essential Requirements:

• Enrolled in a Master's program within engineering, mathematics, statistics, artificial intelligence, data science, or a related discipline.
• A strong quantitative background in statistics, applied mathematics, engineering, machine learning, data science, or a related discipline.
• Experience programming in R, Julia or Python.
• A good understanding of statistical modelling.
• Ability to communicate technical results clearly in written and spoken English.

Desirable requirements:

Experience with generative or probabilistic models, longitudinal data, causal inference, simulation studies, or clinical-trial methodology.

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 November 03, 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

Closing Date