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

Role: Senior Machine Learning Researcher specializing in scientific, domain-aware modeling for agricultural applications. Objectives: develop interpretable, physics- and biology-informed AI models to advance genomic selection and genome editing in crop breeding. Responsibilities: design and validate hybrid mechanistic-statistical ML architectures incorporating biological constraints; develop novel loss functions embedding conservation laws and symmetries; enable predictive modeling of complex traits using genomic, phenomic, and environmental data; implement uncertainty quantification methods; collaborate with geneticists, biologists, and engineers to translate scientific knowledge into scalable models; contribute to scientific publications; document and communicate findings. Requirements: PhD in machine learning, applied mathematics, computational science/engineering, physics, chemical/biomedical engineering, or related with demonstrated research output; proficiency in deep learning frameworks (PyTorch, TensorFlow, JAX); experience with high-dimensional, structured data and scientific computing; strong communication skills. Preferred: 5+ years post-PhD experience; expertise in physics-informed neural networks (PINNs), biology-informed neural networks (BINNs), neural ODEs/PDEs, operator learning methods; experience with Bayesian inference, probabilistic programming, systems biology modeling, genomic data structures, MLOps, and cloud deployment. High-value specifics include focus on agricultural genomics, crop physiology, and environmental dynamics, with application in crop breeding and genome editing. Location: US-based, Missouri (Creve Coeur), remote options possible. Salary range approximately $120k-$170k, with benefits.