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.
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 working (in terms of location) is generally available after consultation and in line with upcoming tasks and (on-site) appointments
- 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, allow 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
- Campus experience: Our research campus in the countryside creates ideal conditions for collegial exchange and sporting activities right on site. Our cafeteria offers a wide range of options—you can enjoy a relaxing lunch break with a lake view
- Successful start: It is important to us that you quickly settle into the team and are given structured training for your tasks. We also support you from the very beginning and make your start easier with our Welcome Days and Welcome Guide
- Fair remuneration: Depending on your existing qualifications and the tasks assigned to you, you will be classified in pay grade 13 of the TVöD-Bund (Collective Agreement for the Public Service). All information on the TVöD-Bund collective agreement can be found on the BMI website. The monthly salaries in euros can be found on page 69 ff. of the PDF download
- Additional benefits: Benefit from attractive additional services such as a company pension scheme with employer contribution. In addition to the basic salary, there is an additional year-end bonus under the collective pay agreement amounting to 75%
- Fixed-term: The position is limited to 3 years
- Support for international employees: Our International Advisory Service makes it easier for international employees to get started
- Career Center: You will receive explicit support with regard to your career development opportunities In addition to exciting tasks and a collegial working environment, we offer you much more: https://go.fzj.de/benefits
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, 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 welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, disability, sexual orientation / identity, and social, ethnic and religious origin. A diverse and inclusive working environment with equal opportunities in which everyone can realize their potential is important to us. The following links provide further information on diversity and equal opportunities: https://go.fzj.de/equality and on specific support options: https://go.fzj.de/womens-job-journey
