Master Thesis: Using Large Language Models and Ontologies to Improve Learning from Historical Safety Incidents

Master Thesis: Using Large Language Models and Ontologies to Improve Learning from Historical Safety Incidents

A Snapshot of Your Day

As a graduate student undertaking a master’s thesis project at Siemens Energy, you will explore how Large Language Models (LLMs) and ontologies can transform historical millwright safety incident reports into structured, reusable knowledge. The goal is to make relevant incidents, recurring hazard mechanisms and preventive controls easier to discover before similar work begins.

• Reviewing candidate safety representation approaches, such as Bow-Tie, FBS, STAMP/STPA, HFACS, energy-barrier models and task-hazard-control models.

• Defining a suitable ontology or hybrid knowledge structure for millwright tasks, tools, components, hazards, exposures, injuries and controls.

• Using an LLM to extract and normalise safety knowledge from structured fields and free-text incident descriptions.

• Building a prototype ontology-based repository, knowledge graph or discovery layer.

• Comparing ontology-based semantic retrieval with the current spreadsheet-based approach.

How You’ll Make an Impact

• Develop a systematic method for representing historical millwright safety incidents as connected, machine-readable knowledge.

• Evaluate how accurately an LLM can extract tasks, tools, components, hazard mechanisms, exposures, injuries and preventive controls from incident reports.

• Help reveal links between similar incidents even when the original reports use different terminology.

• Create and evaluate a prototype that helps users retrieve relevant incidents, hazards and controls for future work planning.

• Provide groundwork for richer safety knowledge reuse, including future support for root cause analysis and the capture of expert tacit knowledge.

• Document the methodology, limitations and results, and communicate findings to technical and non-technical stakeholders.

What You Bring

To be successful in this master’s thesis opportunity, you should have the following qualifications and skills:

• Currently pursuing a master’s degree in Data Science, Machine Learning, Artificial Intelligence, Computer Science, Statistics, Knowledge Engineering or a related field.

• Practical knowledge of machine learning, natural language processing or Large Language Models.

• Interest in ontologies, knowledge graphs, semantic search or structured information extraction.

• Experience with Python and common data science libraries; familiarity with prompt design, structured outputs or retrieval methods is beneficial.

• An analytical mindset and the ability to work with incomplete, inconsistent and ambiguous real-world text data.

• The ability to work autonomously while collaborating with safety specialists, domain experts and data science stakeholders.

• Strong written and verbal communication skills, with the ability to document methods and present findings clearly.

About the Team

You will get an opportunity to work with Advance analytics team, which is one of the teams in IT that is driving the End-to-end responsibility for all central applications and platforms

• Solution Architecture

• Service Management and Application Maintenance

• Operational Provider Management

• Digital Consulting

Implementing all projects concerning central platforms. Orchestrating the governance structure of Business Partner Boards. You will get to know a global company in the forefront of new energy technology. During your time at Siemens Energy you will also get the opportunity to establish network for future cooperation.

Who is Siemens Energy?

At Siemens Energy, we are more than just an energy technology company. With ~100,000 dedicated employees in more than 90 countries, we develop the energy systems of the future, ensuring that the growing energy demand of the global community is met reliably and sustainably. The technologies created in our research departments and factories drive the energy transition and provide the base for one sixth of the world's electricity generation.

Our global team is committed to making sustainable, reliable, and affordable energy a reality by pushing the boundaries of what is possible. We uphold a 150-year legacy of innovation that encourages our search for people who will support our focus on decarbonization, new technologies, and energy transformation.

Find out how you can make a difference at Siemens Energy: https://www.siemens-energy.com/employeevideo

Our Commitment to Diversity

Lucky for us, we are not all the same. Through diversity, we generate power. We run on inclusion and our combined creative energy is fueled by over 130 nationalities. Siemens Energy celebrates character – no matter what ethnic background, gender, age, religion, identity, or disability. We energize society, all of society, and we do not discriminate based on our differences.

Application

Don’t hesitate – apply via https://siemensenergy.avature.net/internalcareers , id nr 306309 not later than 2026-11-23

Ongoing selection is applied, the role might be filled before last application date.

Trade Union Representatives:

Unionen, unionen.finspang.se@siemens-energy.com

Sveriges Ingenjörer & SACO, asi.se@siemens-energy.com

Ledarna, Anders Fors, ledarnaklubbenseab@siemens-energy.com

IF Metall, Mikael Malmgren, mikael.malmgren@siemens-energy.com