Postdoc in Circular and Multi-material Systems for Sustainable Manufacturing

A full-time postdoctoral position is open in the Advanced Production Engineering (APE) research group at the Engineering and Technology Institute Groningen (ENTEG), Faculty of Science and Engineering. The appointment will focus on advancing sustainable manufacturing through circular and hybrid material solutions.

In materials engineering, plastics and metals present complementary strengths and challenges. Metals offer strength, conductivity, and wear resistance while polymers enable complex geometries and lightweight designs. Combining these classes raises issues around recyclability, interfacial adhesion, and mismatched thermal expansion that can induce stresses and affect lifetime performance.

We invite a motivated researcher to develop new methods for integrating circular and multi-material concepts into contemporary production. The project emphasizes analysis and design of multi-material systems including plastics and metals. It will establish an integrated digital framework where materials research and testing provide the validated basis for Digital Twins and AI-driven decision support. The successful candidate will have access to advanced laboratories, simulation platforms, and a multidisciplinary research environment.

We are looking for applicants who hold:

• A PhD degree in Materials Science and Engineering, Production Engineering, or a closely related discipline.
• Strong expertise in materials, computational simulation, AI/ML, or manufacturing process engineering.
• Practical experience with plastics and metals.
• An independent and creative mindset combined with the ability to work collaboratively.
• Excellent English communication skills both written and oral.
• English language proof: IELTS academic = 6.5 or TOEFL computer based = 237 or TOEFL internet based = 92.

As a postdoctoral researcher you will:

• Plan and/or perform experiments to characterize material behavior and processing effects across plastics and selected metals.
• Address variability by identifying and mitigating batch-to-batch differences and ensuring reproducibility.
• Curate high-quality datasets that are structured and annotated for simulation and machine learning.
• Develop Digital Twins and simulation pipelines for virtual validation and predictive performance assessment.
• Implement AI and data-driven methods to connect material properties, processing windows, performance targets, and circularity indicators.
• Collaborate across disciplines with experimentalists, computational engineers, and data scientists to translate lab results into validated digital tools.
• Document and disseminate results through technical reports, peer-reviewed publications, and presentations.

For more information about the APE group please use the following link: www.rug.nl/research/ape

Questions about your application process?

You may contact Friso Salverda, Human Resources Adviser, e-mail: f.d.salverda@rug.nl