Intership | AI-driven quality prediction for polymer extrusion

This internship at TNO focuses on applying artificial intelligence and machine learning to improve quality prediction and control in polymer extrusion processes.

About this position

Plastic recycling plays a crucial role in the transition towards a circular economy, but maintaining consistent material quality remains a major challenge. Recycled polymers often show strong variations in composition, viscosity, and mechanical properties, especially during extrusion processing. This internship at TNO focuses on applying artificial intelligence and machine learning to improve quality prediction and control in polymer extrusion processes. By combining in-line sensor data (such as FTIR spectroscopy, Raman spectroscopy, pressure, and flow measurements) with advanced machine learning models, this project aims to better understand and predict the relationship between extrusion conditions and final material properties. As an intern, you will contribute to cutting-edge research directly supporting more robust and high-quality plastic recycling technologies.

What will be your role?

This internship project focuses on developing machine learning models that link in-line extrusion process data to the final properties of recycled polymers. The goal is to predict material quality (such as crystallinity, hardness or stiffness) using only real-time measurements collected during extrusion, reducing reliance on slow and costly off-line testing.

You will work closely with a multidisciplinary team of scientists specializing in polymers, process engineering, and data science. Your tasks may include data preprocessing, feature extraction from spectroscopy data, model development and validation, and contributing to the interpretation of results from a materials and process perspective. The aim of this internship is to give students hands-on experience in applied industrial research at the interface of AI and sustainable materials.

What we expect from you

Candidates should be currently pursuing a master’s degree in data science, computer science, chemical engineering, materials science, or a related field. Strong programming skills in Python are required, with experience in data analysis and machine learning. Interest in process data, sensors, and physical systems is important. Knowledge of polymers or extrusion processes is a plus. You are expected to be curious, analytical, and able to work both independently and in a team.

What you'll get in return

You want an internship opportunity on the precursor of your career; an internship gives you an opportunity to take a good look at your prospective future employer. TNO goes a step further. It’s not just looking that interests us; you and your knowledge are essential to our innovation. That’s why we attach a great deal of value to your personal and professional development. You will, of course, be properly supervised during your work placement and be given the scope for you to get the best out of yourself. Furthermore, we provide:

• A highly professional, innovative internship environment, within a team of top experts.
• A suitable internship allowance (615 euro for wo-, hbo- and mbo-students, for a full-time internship).
• Possibility of eight hours of free leave per internship month (for a full-time internship).
• A free membership of Jong TNO, where you can meet other TNO professionals and join several activities, such as sports activities, (work-related) courses or the yearly ski-trip.
• Use of a laptop.
• An allowance for travel expenses in case you don’t receive an OV-card.

TNO as an employer

At TNO, we innovate for a healthier, safer and more sustainable life. And for a strong economy. We find each other in wonder and ingenuity. We are driven to push boundaries. There is all the space and support for your talent and ambition. You work with people who will challenge you: who inspire you and want to learn from you. Our state-of-the-art facilities are there to realize your vision. What you do at TNO matters: impact makes the difference. Because with every innovation you contribute to tomorrow’s world.