Make sure you have the required background (see below), identify your preference in terms of topics, and send me an email. Please attach your individual study transcript, including the list of all courses you have completed and still plan to take.
Required background (study programme)
For a thesis in this lab, students should have solid knowledge in one of the following disciplines (good grades in at least 2 courses):
- Algorithmics and Complexity (e.g. Bachelor CSE1305 / CSE2310 / CSE2315, Master IN4301)
- Machine Learning and Data Science (e.g. Bachelor CSE2510, Master CS4220 / CS4070)
In addition, we expect:
- good programming skills;
- knowledge of university-level mathematics: Calculus (e.g. CSE1200), Linear Algebra (e.g. CSE1205), Probability and Statistics (e.g. CSE1210).
This background is readily accessible through BSc + MSc programmes in Computer Science.
For Computer Science MSc students:
- a good start for the research is to do a 7-8 week literature survey (IN4306, 10EC), which consists in writing a review on the state-of-the-art in the chosen research area; this also helps to shape the research questions to be addressed in the thesis;
- following the MSc Bioinformatics specialization is strongly recommended for research topics delving deeper into the application domain, but not mandatory for research topics on more fundamental computational challenges with associated applications in bioinformatics.
Students following other programmes, such as Nanobiology or Life Science and Technology, are advised to follow the above courses as electives in their programmes, or to follow a minor in Computer Science. Exceptions can be made for students showing strong analytical and critical thinking skills.
Topics for Master theses
Examples of thesis topics. All MSc thesis projects are aligned with our current research, or explore new research directions. Most topics are publishable in principle. Potential for publication is highly dependent on the level and quality of the work delivered by the student.