Summary
Are you looking for a job that matters? The transport sector faces a major challenge in reducing weight to improve energy efficiency. Polymer composites offer lightweight solutions, but traditional thermoset composites are difficult to recycle. Thermoplastic composite (TPC) components are rapidly manufacturable and relatively easy to recycle, yet their industrial-scale integration and assembly, especially with fusion bonding, remain underdeveloped. This project aims to establish scientific principles for a physics-based design and production system of advanced assembly methods for TPCs at an industrial scale, resolving the issue of scrapping entire structures due to assembly failures.
Post-doctoral position in Experimental Analysis and Control of Induction Welding Process for Thermoplastic Composites
Designation
Post-Doctoral Candidate
Research Area
- Experimental Analysis and Control of Induction Welding Process for Thermoplastic Composites
- Real-time robust model predictive control schemes for induction welding
- Heat generation in induction welding
- Process control of integrated thermoplastic composite structures
- Physics-based AI meta models for manufacturing processes
Location
University of Twente, Faculty of Engineering Technology (ET), Netherlands
Eligibility/Qualification
- Highly motivated Post-Doctoral candidate.
- Demonstrated experience or strong interest in experimental analysis and control systems.
- Proficiency in data collection and analysis.
- Ability to collaborate effectively within a large international research program (ENLIGHTEN).
Job Description
As a Post-Doctoral candidate, your primary research focus will be on the experimentation of real-time robust model predictive control schemes for induction welding. Initially, you will be responsible for collecting experimental evidence of heat generation using a dedicated setup involving a robot and an induction heating coil. The process control of integrated thermoplastic composite structures is highly dependent on material and process parameters, which will be indirectly extracted through these experiments and in-situ process monitoring. While high-fidelity models describing manufacturing processes and structural performance are often non-linear and multi-scale, your role will involve validating equivalent and efficient physics-based AI meta models and corresponding model predictive control schemes based on collected data. The development of high-fidelity models is already underway within the project, and you will work in collaboration with other PhD students involved in the ENLIGHTEN programme.
How to Apply
Please submit your application using the “Apply now” button on the original vacancy page. Your application should include:
- A cover letter (at most 1 page A4) explaining your specific interests, motivation for applying, and how you qualify for this project.
- A full Curriculum Vitae, including contact information for at least two academic references.
- Transcripts from your Bachelor’s and/or Master’s degrees.
Interviews will be held following the submission closure, with the first online interviews scheduled for October 29.
Last Date for Apply
October 23, 2026
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