Postdoctoral Researcher - Machine Learning Plant Regulatory Genomics

Scientific / postdoctoral posts

Job description: Postdoctoral Researcher - Machine Learning for Plant Regulatory Genomics

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.

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 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, Tensor Flow); 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; 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.

• 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 working (in terms of location) is generally available after consultation.
• Vacation: You will receive 30 days of vacation plus additional days off (e.g., between Christmas and New Year's).
• Flexibility: Flexible working time models, including options close to full‑time, allowing you to tailor your working hours to suit your individual needs.
• Knowledge & further training: Targeted, individual support for your professional development.
• Health & well‑being: Your health is important to us. You can look forward to a comprehensive occupational health management program with a wide range of offerings – e.g., a beach volleyball court, running groups, yoga classes, and much more. In addition, our company medical service and an experienced social counseling team are available to assist you on site.
• Ca…