Theses & Research Projects
Our group offers topics for Bachelor’s and Master’s theses — with Master’s options in Computer Science or Scientific Computing — as well as introductory and advanced research and software projects. Thesis and project topics are closely aligned with our ongoing research.
Prerequisites
Coursework. You should have completed advanced coursework such as Data Science for Text Analytics (IDSTA), Complex Network Analysis (ICNA), or Natural Language Processing with Transformers (INLPT). A solid background in machine learning and natural language processing is essential.
Technical skills. We expect very good programming experience with Python (including pandas and numpy), a good grasp of statistics and linear algebra, and familiarity with Git and Docker. Additional experience with PyTorch, spaCy, gensim, Elasticsearch, or scikit-learn is valuable but not required.
If you have only taken the undergraduate course Introduction to Databases (IDB) and none of the other courses above, it is unlikely that we can accommodate your request.
How to Apply
- Initial contact. Email Prof. Gertz with your transcript, the relevant coursework you have completed, your programming experience, and your research interests.
- Thesis exposé. Write a 4–6 page research proposal covering the context, problem statement, objectives, related work, and a rough timeline of milestones.
- Official registration. Submit the registration forms required by your degree program.
- Supervision. You will meet with your advisor on a bi-weekly basis and drive the research agenda yourself.
Thesis Requirements
- Format. LaTeX is mandatory; English is strongly preferred.
- Structure. The thesis must meet the standards of a scientific paper.
- Presentation. A 20–25 minute public presentation using the provided Beamer templates.
It is your responsibility to ensure that your thesis meets the requirements of a scientific paper. Please note that we cannot accommodate all requests.