Are you interested in applying spectroscopy, chemometrics and process modelling to real-time pharmaceutical manufacturing? This interdisciplinary thesis project will investigate whether NIR and Raman spectroscopy can independently predict all active pharmaceutical ingredients and excipients in a formulation during continuous direct compression.

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:

Real-time monitoring is an important part of advanced pharmaceutical manufacturing. In this project, you will investigate how NIR and/or Raman spectroscopy, together with process information, can provide reliable information about the composition of pharmaceutical formulations.

The work combines pharmaceutical manufacturing, spectroscopy, chemometrics and process modelling, using real formulations and PAT data. You will develop and evaluate modelling approaches and investigate the chemical, physical and statistical factors that affect their performance. If possible, selected approaches will be tested on a continuous direct compression platform, with potential for real-time implementation.

The project is exploratory, and demonstrating that certain formulation components cannot be independently quantified is considered a valuable scientific outcome. The work will be carried out in close collaboration with scientists experienced in PAT, spectroscopy, pharmaceutical processing and data analysis.

Key Objectives:

• Investigate whether NIR and/or Raman spectroscopy can predict every API and excipient in a pharmaceutical formulation in real time.
• Assess whether each component is independently identifiable from the available information.
• Compare individual PLS models (PLS1) with multicomponent models (PLS2).
• Investigate physically constrained models, including non-negative concentration predictions, formulation mass balance and mechanistic models.
• Explore hybrid models combining spectroscopy with process information.
• Design calibration mixtures to separate true spectral information from chance correlations.
• If possible, test selected approaches dynamically on a continuous direct compression platform.

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

Essential Requirements:

• Enrolled in a Master's programme within analytical chemistry, pharmaceutical science or technology, chemical engineering, engineering physics, applied physics, applied mathematics, chemometrics, data science or related discipline.
• Fundamental understanding of spectroscopy and the underlying chemical and physical phenomena.
• Strong interest in multivariate modelling, process modelling, data analysis and automation and experimental design
• Laboratory experience is desirable
• Experience with Python and/or MATLAB is highly desirable.
• Excellent English communication skills, including scientific writing and presentations.

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 October 29, 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