The Genome Data Science lab at the Faculty of Technology is headed by Prof. Dr. Alexander Schönhuth, who has an adjunct affiliation with the Center for Biotechnology (CeBiTec). Our research is centered on the development of computational methods and models that deal with machine learning/data science, data structures, algorithms and statistical models that serve the purposes to arrange, analyze and exploit the rapidly amassing genome data. Thereby, we cover a broad range of algorithms, software and protocols, from primary sequence analysis on the one end to sophisticated algorithms addressing involved questions in genetics, genomics and diseases on the end of high-impact applications.
Education provided by our lab focuses on combinatorial/statistical algorithms and models for the efficient analysis of genome sequencing data, the design of appropriate data structures for putting large amounts of genomes into mutual context (computational pan-genomics), and the design of machine learning (in particular deep learning) architectures and protocols that enable to exploit the rapidly accumulating genome data. While real life impact, for example in terms of truly promoting our understanding about diseases is very important, we put a clear emphasis on the creativeness and the pleasure when designing algorithms, data structures and network architectures that arises in our daily work. Your Tasks
1. research (75 %) in the following fields: metric learning for embedding large genome sequencing data applying deep learning-based generative techniques (e. g., diffusion, flow matching) to generate genome data applying mathematically oriented artificial intelligence techniques performing attention- and state-space model-based methods for processing biomedical data
2. teaching and teaching supporting tasks to the extent of 4 LVS (25 %)
Employment is conducive to academic qualification and provides the opportunity for further academic development. We offer
3. salary according to Remuneration level 13 TV-L
4. fixed-term (3 years, contract extention for 1 or 2 years possible) (§ 2 (1) sentence 1 or 2 WissZeitVG; in accordance with the provisions of the WissZeitVG and the agreement on good employment conditions, a different contract term may apply in individual cases)
5. fulltime
6. internal and external training opportunities
7. variety of health, consulting and prevention services
8. reconcilability of family and work
9. flexible working hours
10. supplementary company pension
11. collegial working environment
12. open and pleasant working atmosphere
13. exciting, varied tasks
14. modern work environment with digital processes
15. various offers (canteen, cafeteria, restaurants, Uni-Shop, ATM, etc.)
Your Profile We expect
16. completed scientific university degree (e. g. Master's degree) in a related subject area
17. very good programming skills
18. experience and sound knowledge of AI
19. ability to work in a team
20. cooperative and team-oriented way of working
21. strong communication skills
22. independent, self-reliant and committed way of working
23. strong organisational and coordination skills
24. strong presentation and moderation skills
Preferred experience and skills
25. good written and spoken English skills
Application Procedure
We are looking forward to receiving your application. To apply, please preferably use our online form via the application button below.
application deadline: Contact
Prof. Dr. Alexander Schönhuth
+49 521 106-3793
Postal Address
Universität Bielefeld
Technische Fakultät
Prof. Dr. Alexander Schönhuth
Postfach 10 01 31
33501 Bielefeld