25 days ago
Stockholm, SwedenEntry Level
Responsibilities
- Review literature on AI for log analysis, explanation generation, and visual analytics.
- Analyze requirements from the MONA LISA and Alstom log-visualization use case.
- Design and implement an AI log-interpretation assistant or small agent architecture.
- Develop a prototype that transforms raw or semi-structured logs into technical summaries or visual explanations.
- Evaluate the prototype for clarity, correctness, relevance, technical usefulness, and traceability.
- Document deployment constraints and potential integration with root cause analysis and solution-support workflows.
- Produce a Master’s thesis report, prototype or demonstrator, evaluation, and recommendations.
Requirements
- Be a Master’s student interested in AI, intelligent systems, multimodal data, explanation generation, log analysis, or visual analytics.
- Relevant academic backgrounds include Computer Science, Artificial Intelligence, Software Engineering, Data Science, Machine Learning, or adjacent technical disciplines.
- Have solid programming ability, especially in Python.
- Have interest in LLMs, agent concepts, retrieval systems, or technical data analysis.
- Be curious about making AI outputs understandable and useful for engineers.
- Be willing to combine research, prototyping, and engineering-domain understanding.
- Experience with logs, data visualization, or industrial software is advantageous but not required.
- Submit a CV, academic transcript, and short motivation statement.
Benefits
- Master’s thesis project combining AI research with practical industrial engineering relevance.
- Opportunity to work with academic and industrial partners through the MONA LISA initiative.
- Access to an active research environment involving AI agents, multimodal retrieval, and visual analytics.
- The opportunity is connected to Alstom’s global transport and mobility organization and its inclusive, equal-opportunity workplace.
Tech Stack
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