PhD opportunity as external doctoral candidate (m/f/d): interdisciplinary (computer science/entrepreneurship) research project on predicting startup success from secondary and trace data

The Technical University of Munich is one of the best universities in Europe. Top performance in research and teaching, interdisciplinarity and talent promotion are its hallmarks. The Chair of Strategy and Organization (Prof. Dr. Isabell M. Welpe) offers a PhD opportunity together with the Frankfurt School Blockchain Center (Prof. Dr. Philipp Sandner). The Frankfurt School Blockchain Center is one of the leading think tanks in the field of Crypto Assets, Decentralized Finance (DeFi), digital Euro and digital securities. The Frankfurt School of Finance & Management is a research-oriented university with a focus on economics and certain areas of business informatics.

Job-ID: GEXT03

The Team

We work in a cooperative and interdisciplinary team on current and internationally relevant research questions in the areas of startup success, innovation and entrepreneurship. In our research we work with quantitative-empirical methods. We publish the results of our research in leading scientific journals and present them at international conferences. We provide our students with the latest scientific and practice-oriented findings. More information about us can be found here:

The Research Project

Predicting which startups will succeed is highly relevant and timely for both academia and practice. Information asymmetries, uncertainty, lack of track record, and other factors make predictions particularly difficult and challenging. Especially in the early stages, there are still no objective criteria for evaluating startups. There is a consensus that personality and team dynamics are crucial factors for the success of startups, but at the same time these have not yet been sufficiently empirically researched, especially with regard to the integration of databases (e.g. Crunchbase, Bloomberg, Dealroom, Pitchbook, Preqin), Startup Analyzers (e.g., Beacon, Bloomberg Beta, FounderNestm, Valuer.AI), secondary data (e.g., GlobalData, SeedTable, StartupDetector, DeepTechInvestorMapping) and trace data (e.g., LinkedIn, CrystalKnows, Humantic, Photofeeler).

Your Tasks

You will conduct theory-driven and quantitative research on current issues, e.g. on the prediction of startup success from personality and team dynamics. This is done in close exchange with members of a research group. Within the research project, there are different research focuses e.g. on the following topics: Entrepreneurial ecosystems, ranking of entrepreneurship performance of entrepreneurial ecosystems and universities, predictors of deep tech startup success and industry-specific startup performance (e.g., B2B. B2C, Biotech), personality and team dynamics of new venture teams, innovative forms of finance and startup performance. Your tasks will include the identification, integration and analysis of different data sources for scientific questions within this project.

Your profile

  • Above-average degree in computer science, business information systems, economics, or a related field (e.g., data science, data analytics).
  • Knowledge and experience in identifying, integrating and analyzing diverse and large-scale data sources (databases, StartupAnalyzers, secondary databases, trace data)
  • Knowledge and experience in building and using complex databases
  • Knowledge and experience in quantitative research and statistical data analysis using R and/or Python, knowledge of machine learning desired
  • Very high general enthusiasm for technology and data
  • Advanced programming skills with hands-on experience in professional and/or in-house projects, preferably with Python
  • Ability to source data (e.g., web scraping, APIs)
  • Ability to generate insights from sourced data sets (classic data analysis techniques, NLP, ML/DL, etc.)
  • Understanding of data, and ability to create meaningful and engaging visualizations
  • Experience working with and designing and operating relational and non-relational database systems
  • Basic knowledge of web development and the associated technology stack
  • Curiosity and motivation to tackle novel tasks independently
  • High interest in research and high motivation for writing scientific articles for publication in international journals
  • Very high commitment and willingness to learn
  • Strong analytical skills
  • The ability to work independently and autonomously in an interdisciplinary team
  • Very good command of written and spoken English
  • Applications from management consultants for doctoral studies in Leave are welcome
  • The TU Munich aims to increase the percentage of women, qualified women are therefore strongly encouraged to apply

We offer

  • Research in an interdisciplinary, motivated and successful research team in the fields of computer science and economics
  • Close involvement in quantitative-empirical research and intensive supervision
  • Participation in international research conferences and, if suitable, stays abroad possible
  • Cooperation with renowned scientists in Germany and abroad as well as with well-known companies
  • A broad spectrum of research areas in the fields of strategy, blockchain, innovation and digitalization
  • Workplace in a central location in Munich
  • Possibility of part-time employment (25–50%) upon agreement, if applicable.
  • The TU Munich aims to increase the proportion of women, qualified women are therefore strongly encouraged to apply
  • With appropriate commitment and dedication, to complete a doctorate, e.g. in the period of leave from a management consultancy, within ~2 years


We look forward to receiving your application documents (cover letter, curriculum vitae, high school diploma, university transcripts, proof of academic achievement, internship/work references, master’s thesis — if available — otherwise bachelor’s thesis) by February 14, 2022, and send them by e-mail in a single pdf file to Please refer to the Job-ID: GEXT03.

Disabled persons will be given preference if their suitability and qualifications are otherwise essentially the same.




Professor | Lecturer | Author | Investor | Frankfurt School Blockchain Center

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Philipp Sandner

Philipp Sandner

Professor | Lecturer | Author | Investor | Frankfurt School Blockchain Center

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