Sr. Machine Learning Researcher, Domain-Aware Modeling & Scientific Machine Learning

At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where 'Health for all Hunger for none’ is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us.

If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.

We are seeking a Sr. Machine Learning Researcher with strong expertise in the mathematical foundations of machine learning and scientific computing to develop next-generation domain-aware models for agriculture. This role combines applied mathematics, domain-aware modeling, and deep learning to build models that encode the underlying structure of biological and environmental systems. You will design interpretable, generalizable AI architectures that integrate scientific knowledge - from genetics to crop physiology to environmental dynamics - into data-driven frameworks.

This work directly enables genomic selection and genome editing target identification, accelerating the development of improved crop varieties worldwide.

• Scientific ML Model Development:
  Design, build, and validate domain-aware machine learning models (e.g., biology-informed, hybrid mechanistic-statistical architectures) that incorporate prior scientific knowledge into learning algorithms for agricultural and genomic applications.
• Mathematical Framework Design:
  Develop novel architectures and loss functions that embed biological constraints, conservation laws, symmetry properties, or known functional relationships to ensure physically and biologically consistent predictions.
• Genomic Selection & Editing Enablement:
  Architect models that leverage high-dimensional genomic, phenomic, and environmental data to predict complex trait outcomes, identify causal genetic variants, and prioritize genome editing targets with quantified uncertainty.
• Uncertainty Quantification:
  Implement rigorous uncertainty quantification frameworks (Bayesian deep learning, ensemble methods, probabilistic surrogate models) to provide decision-makers with calibrated confidence estimates on model predictions.
• Interdisciplinary
  
  Collaboration:
  
  Partner with geneticists, plant biologists, agronomists, environmental scientists, and software engineers to translate domain expertise into model architecture decisions and validate model outputs against biological ground truth.
• Scalable Deployment:
  Work with engineering and IT teams to transition research prototypes into production-grade models integrated within breeding and discovery pipelines, ensuring reproducibility, scalability, and maintainability.
• Documentation & Communication:
  Prepare comprehensive technical documentation, present findings to both technical and non-technical stakeholders, and build organizational trust in AI-driven decision-making.

• PhD in Machine Learning / Deep Learning, Applied Mathematics, Computational Science & Engineering, Physics, Statistics / Probabilistic Modeling, Computer Science (with scientific computing or numerical methods focus), or a related quantitative discipline with demonstrated depth in mathematical modeling.
• Demonstrated research output (publications, thesis work, or applied projects) in scientific machine learning, numerical methods for differential equations, or data-driven modeling of physical/biological systems.
• Proficiency in modern deep learning frameworks (PyTorch,…