In the Department of Computational Biology, gene regulatory networks are generated using machine learning methods and the resulting hypotheses are tested in the laboratory. The research project develops gene regulatory networks using machine learning methods in collaboration with experimental research groups in the USA and UK. Using bulk RNA-seq data and single cell ATAC-seq data, the complex regulatory programme underlying plant responses to abiotic stress is deciphered. A wide range of machine learning methods is applied, with particular emphasis on the interpretability of the resulting models.
The holder of the position to be filled conduct computational experiments to decrypt the transcriptional programme underlying stress responses in plants. The results will be analysed and discussed in close collaboration with the collaboration partners.
Your Tasks
1. Research tasks (90 %): application of machine learning methods to generate gene regulatory networks, including pre- and post-processing as well as extraction of relevant features for prediction comparative analyses in genomes and subgenomes
2. Teaching activities (5 %) participation in teaching activities of the working group
3. Other tasks (5 %) organisational tasks in the working group
Employment is conducive to scientific qualification and provides the opportunity for further academic development. A doctorate on the job is possible.
We offer
4. Salaray according to remuneration level E13 TV-L
5. fixed-term (3 years) (§ 2 (1) of the WissZeitVG; in accordance with the provisions of the WissZeitVG and the Agreement on Satisfactory Conditions of Employment, the length of contract may differ in individual cases)
6. fulltime
7. internal and external training opportunities
8. variety of health, consulting and prevention services
9. reconcilability of family and work
10. flexible working hours
11. 30 days holiday on the basis of 5 working days/week and additional days off on ;and
12. good transport connection
13. supplementary company pension
14. collegial working environment
15. open and pleasant working atmosphere
16. exciting, varied tasks
Your Profile We expect
17. completed relevant scientific university degree (e. g. Master or similar) in Biology, Computer science or any related fields
18. completed relevant PhD (for employment as a postdoc)
19. experience in the application of machine learning methods, proven by relevant publications or theses work
20. advanced knowledge of Python
21. good written and spoken English language skills
22. independent, self-reliant and dedicated way of working
23. strong organisational and coordination skills
24. cooperative and team-oriented mindset
Preferred experience and skills
25. experience in analysing biological data
26. experience in analysing plant data
27. experience in writing scientific publications
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. Andrea Bräutigam
+49(0)521 106-8753
Postal Address
Universität Bielefeld
Faculty of Biology
Prof. Dr. Andrea Bräutigam
Universitätsstraße 25
33615 Bielefeld