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.