M2 Internship - Design and development of MBSE-MDAO model transformation and multidisciplinary simulation data visualization applications
Internship period: Feb to July / 2026
Required profil: M2 / Engineer in Computer Science
Internship Mission Description (see full description in the attached file)
The internship focuses on the development of a model-based integration framework between SysML system models and Multidisciplinary Design Analysis and Optimization (MDAO) workflows. The work is conducted in collaboration with the industrial and academic partners of the research project on agile MBSE and MDAO integration for vehicle design.
The first major objective is to develop a Python-based translation tool capable of converting SysML model elements into executable MDAO code compatible with the Gemseo tool. Specifically, the tool should extract relevant system information—such as parameter diagrams and value properties defined in block definitions—from SysML models created in IBM Rhapsody. Since Rhapsody exports model data in XML format and its API relies on Java or other programming languages, the translation pipeline must take these technical constraints into account to ensure robust data exchange and automation. The ultimate goal is to enable simulation engineers to seamlessly generate preliminary MDAO architectures from SysML inputs, which can then be extended with discipline-specific simulation models.
The second mission is to develop a web-based visualization interface for exploring and analysing MDAO simulation results generated by Gemseo. These results, typically stored in CSV format, will be processed to create interactive visualization dashboards including, but not limited to, Pareto front plots, constraint-variable matrices, and design space exploration controls. The visualization tool aims to facilitate understanding of optimization outcomes and support decision-making during design iterations.
In addition, depending on progress of major objectives, the internship may involve extended work related to SysML v2 transformation and the investigation of potential synergies between modelling languages and AI technologies, such as large language models (LLMs) to enhance model generation, transformation, and automation capabilities in future development phases.