Shaping change: this is what drives us at Forschungszentrum Jülich. As a member of the Helmholtz Association with some 7,600 employees, we conduct interdisciplinary research into a digitalized society, a climate-friendly energy system, and a sustainable economy. We focus on the natural, life, and engineering sciences in the fields of information, energy, and bioeconomy. We combine this with expertise in high-performance computing and artificial intelligence using unique scientific infrastructures.

Plants adapt to their environment through genetic variation, but linking that variation to its ecological role across species remains one of the central challenges in plant biology. If you are passionate about applying deep learning to decode the regulatory grammar of plant genomes and translating predictions into testable biological hypotheses, we invite you to join the Omics Data Analysis and Integration group led by Dr. Jędrzej Szymański. Our group specializes in machine learning, multi-omics data integration, and the development of predictive models for plant gene regulation. We are part of the Institute of Bio

• and Geosciences (IBG-4: Bioinformatics, headed by Prof. Dr. Björn Usadel) at Forschungszentrum Jülich.

The position is embedded in subproject A12 of the DFG-funded Collaborative Research Centre TRR 341 “Plant Ecological Genetics” , a large interdisciplinary consortium spanning the University of Cologne, Forschungszentrum Jülich, and partner institutions.

Join our team

Postdoctoral Researcher

• Machine Learning for Plant Regulatory Genomics You will lead the machine-learning core of an interdisciplinary research project at the interface of genomics, deep learning, and plant biology. Your work will focus on developing and applying predictive models that link genetic variation to gene regulation and traits, working with large multi-omics datasets generated across the consortium. In particular, you will:

Assemble, harmonize, and curate large-scale genomic, transcriptomic, and phenotypic datasets into AI-ready resources, in collaboration with our data-management partners Develop, re-train, and fine-tune deep-learning models for predicting gene expression and transcription-factor binding from regulatory sequences Apply these models to interpret genetic variation, integrate predictions with complementary genetic analyses, and deliver prioritized candidate genes to experimental partners Extend the modeling framework across multiple plant species using transfer learning Present results at consortium meetings and international conferences, publish in peer-reviewed journals, and contribute to science communication and our open-source tools Master's and/or PhD in Computer Science, Bioinformatics, Computational Biology, Data Science, or a closely related field Strong experience in machine learning and/or deep learning, ideally with sequence models (e.g. CNNs, transformers) applied to genomic data Proficiency in Python and common ML frameworks (e.g. PyTorch, TensorFlow); experience working on HPC clusters is an advantage Familiarity with genomics and regulatory biology (gene expression, transcription-factor binding, variant effects, GWAS/eQTL) is desirable; a willingness to expand into population and ecological genomics is essential Structured, analytical thinking and a systematic, careful working method Enthusiasm for interdisciplinary collaboration with experimental biologists and population geneticists across the consortium Excellent English skills (written and spoken); working knowledge of German is a plus We work on highly topical, socially relevant issues and offer you the opportunity to actively shape change! You can expect a wide range of opportunities:



Meaningful tasks: A varied and central role in an international, interdisciplinary environment Work-life balance: Optimal conditions for balancing work and private life, as well as a family-friendly company policy. The option of flexible worki...