You will have a unique opportunity to have impact by shaping how the high-tech equipment industry overcomes the complexity issues in their products and systems through guiding the TNO-ESI research towards the innovation needs of the industry. The strategic direction for TNO-ESI is defined in close consultation with our Partner Board, with representatives from industry, academia, and TNO itself. This partnership is one of the cornerstones of the success of TNO-ESI. In your role you are expected to foster and strengthen this partnership as well as to make it grow further by adding new partners in the coming years.

What will be your role?

• You are deeply passionate about leveraging AI (Large Language Models) to revolutionize system engineering and software development.

• You can conduct assessments of current engineering processes and identify opportunities for AI integration.

• You can develop and present strategic recommendations for AI solutions that enhance system and software design, development, and operational efficiency.

Modern software engineers often need quick, accurate answers about complex codebases. Large Language Models (LLMs) combined with graph-based code representations can provide these answers by leveraging structural and semantic relationships in the code. However, these answers are typically delivered as plain text, making it hard for developers to validate, trust, and act upon them, especially in large, safety-critical systems like the Image Guided Therapy systems developed at Philips.

This thesis addresses the challenge of making LLM augmented with graph-search answers explainable and actionable. Instead of visualizing entire scenarios, the goal is to visualize the evidence and reasoning behind an answer, whether the answer was triggered by a developer’s IDE interaction (via Language Server Protocol signals) or by an explicit query

In a model-based design of a machine’s logistics, such as those captured as LSAT or dataflow models, the scheduling of actions within given activities links closely to activity graphs found in standardized modeling languages like UML/SysML. Computing the start and end-times of nodes in such activity graphs is a key step towards analyzing the overall productivity of a flexible manufacturing system. In this project, the student will adapt and extend the modelling frameworks of LSAT and/or dataflow graphs to the computation of timings with stochastic machine behavior.

We are always looking for students for internships.