Thesis work, 3060 credits Integrate thermodynamic models parameterization with in silico generated data

Are you a student within Chemical Engineering Pharmaceutical Technology Physical Chemistry or Computational Chemistry and interested in applying in silico modeling to accelerate oral drug formulation Join us to develop data-driven parameterization strategies of thermodynamic models for amorphous solid dispersions leveraging COSMO-RS-type descriptors and validating against real phase-equilibria data.

About AstraZeneca:

AstraZeneca is a global science-led patient-centered biopharmaceutical company focusing on discovering developing and commercializing prescription medicines for some of the worlds most serious diseases. But were more than a global leading pharmaceutical company. At AstraZeneca were 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 youll find an environment thats full of unique opportunities and exciting challenges. Here youll 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. Youll be working on meaningful projects to make an impact and deliver real value for our patients and our business.

Thesis work description:

Amorphous solid dispersions (ASDs) are a key platform to enhance the apparent solubility and bioavailability of poorly soluble drugs. Yet their thermodynamic complexityincluding drug recrystallization and amorphous/amorphous phase separation (AAPS)can compromise dissolution and release performance. This thesis aims to develop parameterization strategies for thermodynamic models that leverage in silico data to predict ASD thermodynamic stability reducing dependence on scarce experimental datasets and accelerating formulation development.

Project Objectives:

• Develop a data-driven workflow to estimate pure-component parameters of thermodynamic models for polymers drugs and excipients using computational descriptors.
• Integrate COSMO-RS-type outputs (e.g. σ-profiles σ-potentials) or comparable quantum-chemistry-derived descriptors to map molecular information to model parameters.
• Validate predictions against vapor/liquid equilibria (VLE) and liquid/liquid equilibria (LLE) experimental data and ASD phase maps for selected systems; assess accuracy and transferability.
• Explore the use of molecular dynamics to extract complementary thermodynamic properties and improve model reliability.

Why This Matters:

• Impact: Enable earlier faster and safer decision-making in oral drug product development.
• Innovation: Bridge molecular descriptors with thermodynamic models to predict multi-component ASD behavior (ternary/quaternary systems moisture effects).
• Scalability: Reduce reliance on equilibrium experimental data for novel compounds.

• Structure:
• Duration: 2026
• Credits: 30/60

Essential Requirements:

• Enrolled in a Masters program within Chemical Engineering Pharmaceutical Technology Physical Chemistry Computational Chemistry or related
• Programming knowledge (Python)
• Familiarity with Linux-based environments
• Basic knowledge in thermodynamics concept

So whats 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 cant wait to hear from you!

We welcome your application as soon as possible but ahead of the scheduled closing date 30th of November 2025. 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