Questions about our courses?
- our courses are described in the curricula provided by the PLUS with details on content, requirements, rules of attendance, etc
- for additional questions and help with signing up for courses (passed deadlines), contact our secretary: Karin Mayr-Nestelbacher
Interested in computational systems biology?
We are open for internships, bachelor, and/or master theses for highly motivated students. Our offer:
- a deep dive into how Data Science and Artificial Intelligence is applied to systematically answer biological research questions
- weekly meetings dedicated to the thesis to ensure sufficient feedback
- regular presentations in our lab meeting to hone communication skills
- regular „code review“ meetings to advance technical skills
- flat hierarchies with experienced bioinformaticians to discuss day-to-day questions
- a broad range of projects ranging from analysis of newly collected datasets to method development
- English and international / cutting-edge scientific environment
We recommend to plan and get in touch early on. See below for details. Please sum up relevant completed and oingoing courses (including grades) and especially also relevant interests and computational expertise (a big plus!) in a short application email when considering to join the group.
For students from quantitative disciplines
Students from quantitative disciplines (computer science, data science, artificial intelligence, statistics, mathematics, physics, …) interested in biology and medicine are highly encouraged to apply. Biological background can be mastered during the course of the project. Please get in touch if you are interested to explore possibilities.
For students from biomedical disciplines
From experience, students from biomedical disciplines (biology, medical biology, molecular biology) have little prior experience with computational work. Joining the lab thus presents a steep learning curve, which we are happy to help you with. You should however bring:
- A strong curiosity for statistics and machine learning.
- Ideally prior programming experience or other computational skills.
- A high level of intrinsic motivation and critical thinking.
- Independence (we can only show you the door – you’re the one that has to walk through it).
- Applicants for a master thesis should first perform a practical training in the lab.
To join the lab for a practical training, you should ideally already have completed the following courses (or have equivalent experience):
- Big Data Management (865.M41 / winter semester)
- Bioinformatische Übungen I [English course available!] (614.018 / summer semester)
(this basic programming practical is a soft prerequesite for the following two courses)
- Biomedical Data – From Molecules to Diseases (231.110 / winter semester)
- Hands-on Biomedical Data – Resources and Analysis Tools (231.111 / winter semester)
Further relevant courses are (non-exhaustive list):
- Bioinformatik (614.017)
- Bioinformatische Übungen II (614.026)
- Digitalisierung (614.036)
- Studienergänzung „Informatikkompetenz für alle“
- Introductory statistics lectures