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

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

Employer: Bayer. 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. Sr. Machine Learning Researcher, Domain-Aware Modeling & Scientific Machine Learning 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 sits at the intersection of applied mathematics, domain-aware modeling, and deep learning, with the goal of building models that respect and encode the underlying structure of biological and environmental systems. You will design principled, interpretable, and generalizable AI architectures that integrate scientific knowledge from genetics to crop physiology to environmental dynamics- into data-driven frameworks. Your work will directly enable transformative applications in genomic selection and genome editing target identification, accelerating the development of improved crop varieties worldwide. YOUR TASKS AND RESPONSIBILITIES The primary responsibilities of this role are: - Scientific ML Model Development: Design, build, and validate domain-aware machine learning models (e.g., biology-informed, and 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, conservatio