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).

Deputy Research Manager

As Deputy Research Manager, you will oversee key operational activities, including people management, recruitment and capacity planning. You will also lead Empower conversations to support the professional growth of your team

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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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Bayesian hierarchical and state-space models for machine diagnostics in the high-tech industry

Imagine trying to understand what goes on inside a high-tech machine.
In this project, you will develop high-dimensional discrete state-space models with low-dimensional dynamics.

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