Bachelor / (Executive) Master Thesis at the Center for Strategy and Organization
📌 Key facts
The objective of this IDP project is to create a Natural Language Processing (NLP) and/or web scraper tool to develop a “hardware intensity” measure for every start-up in our dataset based on different available sources like patents, company websites or PitchBook metadata.
- When: Start anytime. Applications are open!
- How to apply: Send us an e-mail (at the end of this page) with your CV, grade report and research interest.
- 📌 Key facts
- 💡Background
- 🦾Who We Are
- 🎯 Goals and Topics
- 🎓 Profile
- ➕ Additional Information
- 📝 How to Apply
- 📬 Contact
💡Background
Semiconductors are a foundational technology often described as “the oil of the 21st century” and powering everything from AI and data centers to automotive and industrial systems. At the same time, semiconductor startups operate in a highly complex industry, facing distinct commercialization and financing challenges that might differ from many other high-growth tech ventures. A clearer understanding of how venture capital interacts with semiconductor startup strategies, innovation trajectories, and ecosystem dynamics - and how these interactions can alleviate bottlenecks and enable the next wave of innovation - is highly relevant for entrepreneurs, policymakers, and researchers alike.
🦾Who We Are
I am a Senior Consultant at McKinsey & Company and PhD student at the Chair focusing on deep tech entrepreneurship. My research examines how deep-tech ventures, especially in strategically important domains such as semiconductors, are financed and scaled, and which innovation signals, investor behaviors, and ecosystem conditions shape their funding trajectories and commercialization success. My educational background is in Business and Management.
As Chair for Strategy and Organization we are focused on research with impact. This means we do not want to repeat old ideas and base our research solely on the research people did 10 years ago. Instead, we currently research topics that will shape the future such as Deep Tech, (Gen) AI, Digital Transformation and Business Model Innovation, Diversity, Leadership, and Teams. We are always early in noticing trends, technologies, strategies, and organizations that shape the future, which has its ups and downs.
🎯 Goals and Topics
We are building a global, data-driven view of semiconductor startup ecosystems and venture capital activity using quantitative methods. Our core dataset covers 3,000+ startups, 7,000+ funding events, and 10,000+ investors, and we continuously enrich it with additional sources like patent details. Potential topics within this research stream include but are not limited to:
- Building a “Hardware Intensity” Measure Develop a scalable way to classify semiconductor startups by hardware intensity and commercialization complexity based on company and technology information.
- Funding Journeys in Semiconductors Create a stage-by-stage view of VC financing in semiconductors (progression rates + timing), and identify the main bottlenecks by geography and startup segment.
- Investor Playbooks in Semiconductors Compare investor types and participation patterns (e.g., specialist vs. generalist, corporate/strategic vs. financial) across different semiconductor startup categories.
- Innovation Signals from Patents Build patent-based indicators to characterize startup innovation profiles and benchmark how they differ across segments and ecosystems.
🎓 Profile
Students should have a strong academic background and passion for (deep-tech) entrepreneurship. Previous experience in Semiconductors or related domains is a benefit, but not needed. Hands-on experience with Python or other data analysis software is recommended. Students should have a strong academic track record, good communication skills, and display a high degree of independence.
We do not want your research to gather dust in some corner of bookshelf but make it accessible to the world.
➕ Additional Information
We have a strong base set of high-quality data available for students to work with, although additional quantitative data gathering and validation will be expected to enrich the existing dataset.
In some cases, multiple students may be jointly developing/validating the same datasets. Collaboration will be encouraged where feasible.
Next to being able to supply data, we are well-positioned in the German and Bavarian semiconductor technology environment to connect students to researchers, startups, etc.
📝 How to Apply
If you are interested, please contact Henri Stehle via email (Subject: Application Semiconductors & Venture Capital Thesis - [Name]) by submitting your CV and grade report. Please also briefly outline your tentative research idea/interest in the email, as well as your ideal start date.
We're greatly looking forward to hearing more about you!
📬 Contact
Henri Stehle (Chair for Strategy and Organization)