The Faculty of Technology has the following job opening: 
Employment is conducive to academic qualification.
Research assistant (PostDoc) - Genome Data Science
ID: Wiss25228
- starting: as soon as possible
- Vollzeit
- salary according to Remuneration level 13 TV-L
- befristet
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.
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
- Research (75 %) in the following field:
- implementation of optimal transport-based techniques
- processing of discrete Ricci flows and the corresponding clustering methods
- metric learning for embedding very large bacterial sequence 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
- Teaching and teaching supporting tasks to the extent of 4 LVS (25 %)
Employment is conducive to academic qualification.
We offer
- salary according to Remuneration level 13 TV-L
- befristet (3 years) ( § 2 para. 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)
- Vollzeit
- internal and external training opportunities
- variety of health, consulting and prevention services
- reconcilability of family and work
- flexible working hours
- supplementary company pension
- collegial working environment
- open and pleasant working atmosphere
- exciting, varied tasks
- modern work environment with digital processes
- various offers (canteen, cafeteria, restaurants, Uni-Shop, ATM, etc.)
Your Profile
We expect
- completed scientific university degree in a related subject area
- completed or advanced doctorate in a related subject area
- very good programming skills
- experience and sound knowledge of AI
- ability to work in a team
- cooperative and team-oriented way of working and strong communication skills
Preferred experience and skills
- good written and spoken German and English skills
- independent, self-reliant and committed way of working
- strong organisational and coordination skills
- strong presentation and moderation 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: 24.07.2025
Contact
Prof. Dr. Alexander Schönhuth
0521 106-3793
sklusmann@cebitec.uni-bielefeld.de
Postal Address
Universität Bielefeld
AG Genome Data Science
Prof. Dr. Alexander Schönhuth
Postfach 10 01 31
33501 Bielefeld
Prof. Dr. Alexander Schönhuth
0521 106-3793
sklusmann@cebitec.uni-bielefeld.de
Postal Address
Universität Bielefeld
AG Genome Data Science
Prof. Dr. Alexander Schönhuth
Postfach 10 01 31
33501 Bielefeld
Bielefeld University has received a number of awards for its achievements as an equal-opportunity employer and has been
recognized as a family-friendly university. The university welcomes applications from women. This is particularly true with regard
both to academic and technical posts as well as positions in information technology as well as the skilled crafts and trades.
Applications are handled according to the provisions of the state statutes on equal opportunity. Applications from suitably qualified
handicapped and severely handicapped persons are explicitly encouraged.
At Bielefeld University on request positions can be carried out with reduced working hours as long as this does not conflict with official needs.
