Student Thesis: Capturing expert knowledge & reasoning

The Bundesanstalt für Materialforschung und -prüfung (BAM) is a senior scientific and technical federal institute with responsibility to the Federal Ministry for Economic Affairs and Energy. It tests, researches, and advises to protect people, the environment and material goods.

The division of Transport Container Safety is concerned with research, testing and assessment of transport containers for radioactive materials. This includes mechanical and thermal safety verifications and the development of experimental and computational test methods for the analysis of transport containers.

Topic of the Thesis

Experienced engineers at BAM review the safety of transport packages for radioactive material. Their expertise has grown over many years of practice. It will be captured and turned into a structured, machine-readable reference set. Your thesis lays the groundwork for this.

Task of the Thesis

You research how expert knowledge can be captured reliably and turned into an open, machine-readable knowledge format, using AI-assisted interviews. You propose a practical workflow, then demonstrate it in a prototype and evaluate it on real recordings.

Agenda

• The thesis can be written in English or German
• Compare capture methods, such as interviews, thinking aloud during work, and walking through a finished case afterwards
• Propose a workflow and justify your choice
• Build a prototype. Recordings are transcribed automatically and structured into single reasoning steps with an LLM
• Build a simple validation interface. The model proposes, the expert confirms or corrects
• Measure the results. Transcription quality, structuring accuracy, invented content, and expert time per case

Qualifications

• Studies in computer science, data science, engineering, human factors, engineering psychology, cognitive science, or a related field
• You can build working prototypes with AI coding assistants (“vibe coding”).
• Solid skills in a language like Python are a plus, not a must
• Interest in Open Knowledge Format (OKF), LLMs and speech recognition
• Good German listening comprehension, because the recordings are in German
• Helpful but not required: experience with prompt engineering or think-aloud studies

Contact

Mohamed Tababi , phone: 030 8104-3674, E-Mail: Mohamed.Tababi@bam.de
Kutlualp Tazefidan, phone: 030 8104-3827, E-Mail: kutlualp.tazefidan@bam.de
Dr. Tobias Gleim, phone: 030 8104-3168, E-Mail: Tobias.Gleim@bam.de

Address

Bundesanstalt für Materialforschung und -prüfung (BAM)
Division 3.3 Safety of Transport Containers
Unter den Eichen 44-46, 12203 Berlin