• Topic: AI-Driven Project Risk Prediction & Process Optimization using Historical "Lessons Learned” Data. In R&D Excellence, you will develop a data-driven approach to predict project risks and delays and uncover process improvement opportunities by leveraging historical project information and Lessons Learned data. The thesis aims to transform underused, unstructured Lessons Learned knowledge into structured, explainable insights that support governance and decision-making.
• Automate project status tracking using historical project and review data
• Convert LL repositories into structured, predictive intelligence
• Build explainable ML models to predict risk and delay probability
• AI-based Status & Risk Prediction Engine
• Lessons Learned Analytics Module (themes, clusters, insights)
• Visualization dashboard with risk themes & improvement recommendations

• Enrolled Master’s student in Data Science, Computer Science, AI/ML, Industrial Engineering, or similar
• Structured, analytical mindset; motivation to translate data into actionable insights
• Understanding of project management principles
• Solid coding skills in Python (data handling, ML basics)
• Interest or experience in NLP/text mining (nice to have)
• Skilled in Microsoft Office (especially PowerPoint and Excel); familiarity with SharePoint, Jira/Confluence, Power BI, or AI-enabled productivity tools is a plus
• Excellent English skills (German is a plus, depending on data sources and stakeholders)Strong interest in business process management, digitalization, and IT tools
• Strong interpersonal skills and a solution-oriented approach

We offer competitive salaries and additional benefits based on your performance, experience, and qualifications. Employment is in accordance with the collective agreement for the electrical and electronics industry, employment group E (https://www.feei.at/aktuelles/mindestloehne-und-gehaelter-eei/).

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