Domain-Aware ML Researcher for Genomics & Agri-Systems

Bayer; St. Louis (MO); Sur place; USD 120 000 - 170 000; Plein temps

Résumé du poste

Bayer in Creve Coeur, Missouri, seeks a Sr. Machine Learning Researcher to develop domain-aware models for agriculture.

You will design biology-informed architectures that integrate genetics, crop physiology and environmental dynamics into data-driven frameworks, accelerating genomic selection and genome editing targets. Collaborate with geneticists, plant scientists and engineers to translate domain knowledge into scalable AI, validate outputs against ground truth, and transition prototypes

Qualifications

• PhD in ML/DL or related quantitative field with deep modeling work.
• Publications/thesis in scientific ML or data-driven modeling.
• Proficiency in PyTorch, JAX or TensorFlow; ability to work with scientific computing tools.
• Experience with high-dimensional, multi-modal data and domain knowledge.

Responsabilités

• Design, build, and validate domain-aware ML models for agriculture.
• Develop physics/conservation-informed architectures and loss functions.
• Leverage genomic, phenomic, and environmental data to predict traits.
• Implement uncertainty quantification for calibrated predictions.
• Collaborate with geneticists and engineers to deploy production-grade models.
• Document results and communicate with technical and non-technical stakeholders.

Connaissances

PhD in ML/DL or related quantitative</

Deep learning frameworks

Domain-aware modeling

Scientific computing

High-dimensional data handling

Communication skills

Interdisciplinary collaboration

Formation

PhD in machine learning / related quantitative discipline

Outils

PyTorch

JAX

TensorFlow

Description du poste

Bayer in Creve Coeur, Missouri, seeks a Sr. Machine Learning Researcher to develop domain-aware models for agriculture.

You will design biology-informed architectures that integrate genetics, crop physiology and environmental dynamics into data-driven frameworks, accelerating genomic selection and genome editing targets. Collaborate with geneticists, plant scientists and engineers to translate domain knowledge into scalable AI, validate outputs against ground truth, and transition prototypes