General Info
We can best supervise bachelor or master theses if the topic is related to our research. Therefore, we recommend applicants to first explore our research in order to propose a related topic. We expect a strong interest in working with data in order to theoretically explain innovation and entrepreneurship phenomena. Depending on the applicants' background, topics can also focus on technical aspects in the area of data science, machine learning, natural language processing, network analysis or econometrics. Ideally, applicants find an interesting topic among those suggested below.
We are open to collaborative thesis projects with startups and corporates, preferably under two conditions: (1) Entrepreneurial focus, i.e. projects imply a market-oriented change of company offerings w.r.t. target customers, product features, pricing, marketing or sales. (2) Empirical focus, i.e. the entrepreneurial change can be (a) experimented with in terms of A/B testing, (b) analyzed based on existing data about its potential outcomes, or (c) evaluated on a qualitative, strategic level by thoroughly interviewing stakeholders. Thesis projects involving purely conceptual work without any empirical evaluation or only secondary research about state-of-art, best-practices, competitor benchmarks or market intelligence do rather not qualify.
The first step for applicants is to choose or propose a thesis topic (based on our research or a company collaboration) by submitting an abstract describing the topic, how to approach it and the applicant's backround, via the contact form.Open Topics
AI in Circular Economy Research Benchmarking
Master’s Thesis | Data Science & Innovation
This thesis offers a unique opportunity to work at the intersection of sustainability, artificial intelligence, and data science. You’ll develop cutting-edge methodologies to identify, curate, and validate best practices in circular economy research using advanced data analytics techniques.
Key Focus Areas:
- Data-driven best-practice identification using machine learning algorithms
- Best-practice curation through automated content analysis
- Validation of best-practice recommendations via empirical testing
- Community-of-best-practice exchange platform development
Ideal for students with: Background in data science, computer science, business informatics, or environmental studies with strong analytical skills and interest in sustainability.
Contact: Jonas Wilinski (jonas.wilinski@tuhh.de)
Circular Economy,Clean Technologies & Sustainability Innovation Research
Master’s Theses | Quantitative & Qualitative Research Methods
Join our research institute at the intersection of Management Science and Deep Learning to investigate critical questions in clean technologies, green innovation, and circular economy transitions. We offer flexible thesis opportunities where you can pursue your specific interests using diverse methodological approaches (quantitative (e.g. Deep Learning or NLP) or qualitative (e.g. Interviews or Literature Reviews)) and comprehensive datasets (e.g., patent data, scientific publications, startup databases).
Potential Key Focus Areas:
- Technology emergence and novelty detection in sustainable innovations
- Diffusion patterns and commercialization of clean technologies
- Circular economy transformation through technological innovation
- Data-driven analysis using PATSTAT, OpenAlex, and startup databases
- NLP/Deep Learning or qualitative methods (interviews, literature reviews)
Ideal for students with: Backgrounds in management, computer science, data science, engineering, or sustainability studies with strong analytical interests and passion for the green transition.
Contact: Jürgen Thiesen (juergen.thiesen@tuhh.de)
Taxonomy Generation for Emerging Research Fields
Master’s Thesis | Natural Language Processing & Research Analytics
This thesis focuses on developing advanced NLP techniques to automatically generate comprehensive taxonomies for small, emerging research fields. You’ll work with cutting-edge language models and contribute to the methodological advancement of research field analysis.
Key Focus Areas:
- Advanced NLP techniques for automated taxonomy generation
- Analysis of existing taxonomy generation papers and methodologies
- Development of novel approaches for small research field classification
- Validation and benchmarking of taxonomy quality metrics
Ideal for students with: Strong background in computer science, computational linguistics, or data science with interest in academic research and natural language processing.
Contact: Jonas Wilinski (jonas.wilinski@tuhh.de)
