Overview
The Network Analysis and Modelling group investigates how genetic variation shapes gene regulation, protein function, and observable plant traits. Using deep learning and network analysis, we aim to enhance crop performance through discovery and annotation of regulatory variants that guide breeding and gene‑editing approaches.
Position
Postdoctoral Researcher within the BMFTR‑funded project Twin – A Digital Discovery Platform for Cereal Genetic Resources. The role focuses on inferring the cereal pan‑regulome and its functional variation in wheat and barley.
Responsibilities
- Design, train, and interpret deep‑learning models to infer regulatory sequence features across barley and wheat genomes.
- Integrate genome assemblies, WGS variant calls, and large‑scale RNA‑seq data.
- Quantify the functional impact of regulatory sequence variation (SNPs, indels, SVs) and integrate the results with GWAS and trait data.
- Contribute to the open RegulomeAtlas resource, exposing results through REST and BrAPI‑compatible APIs.
- Ensure FAIR data management and collaborate closely with geneticists, breeders, and industry partners; publish results in high‑impact journals.
- Supervise MSc/PhD students and contribute to new grant proposals.
Qualifications and Skills
- PhD in Bioinformatics, Computational Biology, Genomics, or a related field.
- Proven expertise in deep learning and statistical modelling of biological sequence data.
- Experience with genomic and transcriptomic (RNA‑seq) data; familiarity with cis‑regulatory elements, gene regulation, or pan‑genomics is advantageous.
- Confidence in using HPC environments, version control, and FAIR principles.
- Excellent English communication skills and a strong publication record.
Fit
- Strong interest in plant genomics, gene regulation, and deep learning.
- Scientific curiosity and motivation.
- Ability to work autonomously and in a team.
Offer
- Dynamic research environment with state‑of‑the‑art facilities and many growth opportunities.
- International, interdisciplinary team that values open communication and flat hierarchies.
- Collegial atmosphere supporting work‑life balance and flexible arrangements.
- Project‑based position starting 1 September 2026, limited to 2 years.
- Gross salary up to 100 % E13 TV‑L.
Equality and Diversity
For us, your qualifications and strengths count. All applicants irrespective of gender, origin, age, or disability are welcome. The IPK strives to increase the proportion of women in underrepresented sectors and explicitly encourages qualified women to apply. IPK offers family‑friendly working conditions and flexible hours. Qualified applicants with a disability are given preference.
Contact
Dr. Jedrzej Jakub Szymanski
Tel.: +49 39482 5-753
Application
Submit a complete online application (letter of motivation, CV, certificates) as a single PDF to https://www.ipk-gatersleben.de/en/career/job-offers before 22 July 2026. Indicate reference number 28/06/26 in your correspondence. Incomplete documents will not be considered. Foreign qualifications must be validated via the German equivalence test (https://zab.kmk.org/en/statement-comparability).
Postdoctoral Researcher (f/m/d) in the field of Deep Learning for the cereal Gene Regulation Arbeitgeber: IPK Leibniz-Institut
Das IPK bietet Ihnen die Möglichkeit, in einem internationalen und interdisziplinären Arbeitsumfeld zu arbeiten, das an der Schnittstelle zwischen Lebenswissenschaften und Informatik liegt. Sie werden Teil eines großen Projekts in einer freundlichen und familiären Atmosphäre, wo Ihre Fähigkeiten geschätzt werden und Sie die Chance haben, sich beruflich weiterzuentwickeln. Zudem fördern wir familienfreundliche Arbeitsbedingungen und flexible Arbeitszeiten, um eine ausgewogene Work-Life-Balance zu gewährleisten.
