Thesis

Req ID:524032 

At Alstom, we understand transport networks and what moves people. From high-speed trains, metros, monorails, and trams, to turnkey systems, services, infrastructure, signalling and digital mobility, we offer our diverse customers the broadest portfolio in the industry. Every day, more than 80 000 colleagues lead the way to greener and smarter mobility worldwide, connecting cities as we reduce carbon and replace cars.

We are offering a Master’s thesis opportunity in collaboration with academic and industrial partners contributing to the MONA LISA research initiative, which focuses on monitoring and analytics across the whole lifecycle of cyber-physical systems, from concept and models to deployed systems. The thesis is aimed at students who want to work at the intersection of AI, software engineering, industrial analytics, and trustworthy decision support. 

The proposed topic centres on AI-agent support for root cause analysis in complex engineering environments. In the documented Alstom / MONA LISA use case, AI is intended to support engineers by combining evidence from issue tickets, logs, product documentation, historical cases, source code, and engineering workflow items in order to shorten analysis time and improve issue handling. The long-term ambition is not to replace engineering expertise, but to provide AI support that helps engineers navigate fragmented information more efficiently and with better traceability.

Background

Engineering organisations working with safety-critical and high-complexity systems often analyse faults and incidents across several disconnected tools and artefacts. In the Alstom / MONA LISA use case for railway systems, the target workflow includes AI-supported log visualisation, root cause analysis, and solution proposal development. The use-case document explicitly describes how an AI agent can retrieve and combine information from Dr MITRAC, Dimensions, SharePoint, GitLab, and EWM in order to support analysis of reported product issues. 

This challenge is highly relevant because the available information is distributed over different sources, formats, and abstraction levels. Engineers may need to combine operational log patterns, issue descriptions, historical defects, architecture information, source code references, and workflow items before they can form a reliable hypothesis about the most likely cause of an incident. This makes the problem an ideal candidate for AI-agent orchestration, where specialised components can gather, interpret, and validate evidence from different sources before presenting a structured explanation for expert review.