🌿 Postdoctoral Researcher | Machine Learning in Plant Genomics
📍 Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
📅 Deadline: 25 July 2025
🔗 More Info & Apply Here
The Network Analysis and Modelling Group at IPK Gatersleben is inviting applications for a Postdoctoral Researcher (f/m/d) to develop and apply machine learning approaches to rapeseed genomics and breeding.
We investigate how genetic variation shapes regulatory mechanisms and phenotypic traits, and use AI tools to accelerate crop improvement.
🧬 Your Role
As a key team member, you will:
- Design and train deep-learning models to predict regulatory gene variants in rapeseed
- Integrate large-scale sequence and RNA-seq data from public and internal resources
- Build a reference library of regulatory motifs and transcription factor networks
- Use random forest models and network analysis to uncover gene-editing targets
- Perform multi-omics integration to detect genotype–phenotype associations
- Ensure FAIR data practices, co-publish high-impact results, and supervise students
- Collaborate with breeders, geneticists, and industry partners to enable translation
🔍 What We Offer
- Cutting-edge research at the intersection of machine learning and plant genomics
- Access to rich internal datasets and experimental collaborations
- Mentorship in grant writing and leadership opportunities
- A collaborative environment with access to leading experts in genomics and AI
🎓 Your Background
We are looking for a candidate with:
- A PhD in Bioinformatics, Computational Biology, Genomics, Data Science, or similar
- Experience with machine learning, network analysis, and multi-omics data integration
- Proficiency in Python, R, or relevant ML frameworks
- Interest in plant science, gene regulation, or crop improvement
- Strong communication skills and a collaborative mindset
📄 How to Apply
Please submit your online application by 25 July 2025 via: Link Here!
Join us in building the future of intelligent crop improvement through AI-powered plant genomics.