Post-doctoral position in Experimental Analysis and Control of Induction Welding Process for Thermoplastic Composites of The...

Job description

The challenge
Weight is a major obstacle in making the transport sector green, as extra mass means extra energy consumption. Polymer composites offer light weight solutions to potentially reduce 20-30% of mass. However, current generation (thermoset) solutions are hard to recycle. Thermoplastic composite (TPC) components, instead, can be produced rapidly and recycled relatively easily. The major challenge, however, is in scaling up both the series size and the physical dimensions of the structures in a viable and sustainable way. While the manufacturing of individual parts is at a high level of maturity, the integration and assembly of these parts is far less developed, particularly with fusion bonding process (the preferred joining method). In this process, failure of assembly implies scrapping the entire structure. This project is trying to resolve this problem, by establishing the scientific principles for a physics-based design and production system of advanced assembly methods for TPCs at an industrial scale.
Job description
We are looking for a highly motivated Post-Doctoral candidate to join our research team. Your research focus will be on the experimentation of real-time robust model predictive control schemes for induction welding. As a first step you will be collecting experimental evidence of the heat generation using dedicated experimental setup with a robot and an induction heating coil. The process control of integrated thermoplastic composite structures is critically dependent on the material and process parameters, which are indirectly extracted via the aforementioned experiments and in-situ process monitoring. On the other hand, the physics-based models describing manufacturing process and structural performance are of nonlinear and multiscale nature. As those high fidelity models are not suitable for control applications, equivalent and efficient physics-based AI meta models, and corresponding model predictive control schemes will be validated based on the data collected. The development of the high fidelity models are already under progress within the project. The work will be done in collaboration with other PhD student already employed within ENLIGHTEN programme (Participants: Airborne, Airbus, Aniform, Autodesk, Boeing, Boikon, Bosch, Cato, Composites NL, DSM, DTC, Engel, e‐Xstream, GKN/Fokker, HAN University of Applied Sciences, Saxion University of Applied Sciences, JLR, KVE, M2i, Province of Overijssel, SAM|XL, SET Europe, Solvay, Delft University of Technology, Eindhoven University of Technology, TNO‐BMC, Toray Advanced Composites, TPRC, University of Twente, University of Warwick, and Victrex).

Requirements

• A PhD degree in Mechanical Engineering (composite materials, experimental mechanics, data analysis, experimental control and related).
• Experience with experimental methods and data acquisition systems like LabView or similar systems will be beneficial
• Strong, proven programming skills in Python.
• Proficient in written and oral English.
• The ability to operate at the intersection of various fields.
• A high degree of responsibility and independence, able to collaborate with colleagues, researchers, and other university staff.
• Experience with finite element libraries and numerical methods would further strengthen the application.

Additional information

Please submit your application before 23 October 2026 using the “Apply now” button, and include:

• A cover letter of at most 1 page A4, explaining specific interests, the motivation for the application, and why you qualify for this project.
• A full Curriculum Vitae, including contact information for at least two academic references
• Transcripts from your Bachelor and/or Master degrees

Interviews will be held following the submission closure. Additional information about this position can be acquired from Prof. Bojana Rosic (b.rosic@utwente.nl).
The first (online) jobinterviews will be held October 29.
Screening is part of the procedure