The Engineering Dynamics | Engineering Dynamics | Department MS3](https://www.utwente.nl/en/et/ms3/research-chairs/ed/)) group, Faculty of Engineering Technology at the University of Twente, The Netherlands has an opening for a postdoc candidate within the project “Scale-up PIC based sensing PhotonDelta FBG Sensing)”.
The developments of photonics based systems have taken the technology to a next level in the past years. In particular photonics based sensing systems have made their way to applications. To further push the application of these systems, a number of steps need to be taken. One of these steps is the reduction of components, increasing the manufacturability and integration capability, while decreasing the cost price and not compromising the accuracy.
For high-accuracy operation of a Photonic Integrated Circuit (PIC) based module, handling PIC Temperature dependency is key. Typically, operation and calibration at a steady temperature setting is used, implying the need for a PIC thermal control means (TEC) in operation. Alternatively, a software compensation is used, but can be computation/memory heavy considering nonlinearities in the thermal responses, which are also unit-specific, thus forming fundamental challenges. Physics-based models are too complex, while pure data driven models lack explainability, ultimately reducing the robustness.
The proposed solution is the development of a Physics Informed Machine Learning method which exploits the advantages of physics-based and data-driven models, while mitigating the disadvantages. This research will contain experimental and modelling. The modelling focusses on identifying the key physical relations between the temperature and complex/compounded photonic circuitry, and the sources of variation of this relation. These variation range from uncertainties and heterogeneities in material properties, inaccuracies and variability occurring during the production and assembly phase of the photonic chips and so on. The experimental work will be done in close collaboration with the industrial partner PhotonFirst and aims to quantify model parameters and their variations as such providing data for the Physic-Informed Machine Learning model.
Information and application
You can apply for this position before 1st of September, 2026 by clicking the ‘apply now’ button below. Please include:
- CV (including contact information for at least two academic references)
- An original motivation letter indicating your fit with this position and associated ambition.
The first job interviews are planned for mid September.
For more information on this position, please contact dr.ir. Richard Loendersloot, r.loendersloot@utwente.nl.
Screening will be part of the selection procedure.
About the organisation
At the Faculty of Engineering Technology (ET), we work on engineering for impact: developing smart, sustainable, human-centred and technological solutions for societal challenges. We connect fundamental education, research and practice across five core domains: Asset & Maintenance engineering, Intelligent Manufacturing Systems, Personalised Health Technology, Resilience Engineering, and Sustainable Production, Energy and Resources.
We work on education and research in mechanical engineering, civil engineering and industrial design engineering. Together, we learn by making, creating, and innovating, addressing challenges in a solution-oriented way. Quality, connection and inclusivity are the foundation of our culture.
In our open community, students, researchers and staff collaborate with industrial and societal partners. This enables us to develop insights, applications and solutions that add value to society.
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