Careers

Careers

TNO-ESI as an employer

TNO-ESI is always looking for talent to reinforce our team of research fellows.

If you have a relevant background in computer science, electrical engineering or applied mathematics, have a strong affinity with applied R&D in an industrial context and looking for the next step in your career, feel free to send your open application, including your CV and a motivation letter to the TNO-ESI Science Director Wouter Leibbrandt and the recruiter Yvonne Pribnow, (contact details below).

Scientist inference for complex systems

As a scientist operating from industry labs at high-tech giants, you bridge the gap between academia and industrial practice in applied research projects. You share insights that you gain during your research with industrial partners and science.

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Senior Project Manager Applied Research

You will have a unique opportunity to have impact on how the high-tech equipment industry overcomes the complexity issues in their products and systems through managing projects at TNO-ESI. In such projects, TNO-ESI creates impactful and industrially applicable architecting and design methodologies.

Internships

We are always looking for students for internships.

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Simulating high-mix low-volume manufacturing systems

As HMLV systems’ characteristics are different from traditional mass production systems’, dedicated methods are needed to reason about these systems. In this assignment, we want to work out a method to model and analyze the performance of HMLV systems using simulation tooling. The method should be lightweight, such that it can easily be adopted by HMLV companies that are typically SMEs.

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Investigating the Best Practice of Research OSS

The candidate will investigate several research questions: What are the challenges faced by open-source research projects and what are the current practices that address them? What are the factors, inner workings and practices that influence the success or failure of open-source research projects?
This research will provide actionable insights about best practices of open source research software based on empirical evidence collected by combining qualitative and quantitative methods.

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