> Markdown version of [/jobs/ext/1104730-sr-machine-learning-researcher-domain-aware-modeling-scientific-machine-learning](https://www.wearedevelopers.com/jobs/ext/1104730-sr-machine-learning-researcher-domain-aware-modeling-scientific-machine-learning). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Machine Learning Researcher, Domain-Aware Modeling & Scientific Machine Learning - **Company:** Bayer AG - **Location:** Creve Coeur, MO, United States - **Experience:** Expert - **Salary:** $120,000.0 - $170,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Artificial Neural Networks, Cloud Computing, Data Structures, Dynamical Systems, Machine Learning, Population Genetics, Tensorflow, Scientific Computating, Pytorch, Deep Learning, Gaussian, Containerization, Information Technology, Data Analytics, Machine Learning Operations - **Published:** June 14, 2026 - **Apply:** https://www.agcareers.com/bayer/sr-machine-learning-researcher-domain-aware-modeling-amp-scientific-machine-learning-job-1030956.cfm ## About the Role + Machine Learning / Deep Learning * + Applied Mathematics * + Computational Science & Engineering * + Physics * + Chemical, Mechanical, or Biomedical Engineering * + Computer Science (with scientific computing or numerical methods focus) * + Statistics / Probabilistic Modeling * + Another 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, JAX, or TensorFlow) and scientific computing libraries. * Experience formulating and solving problems involving high-dimensional, structured, or multi-modal data. * Strong communication skills and willingness to collaborate across disciplines. Preferred: * 5+ years post-PhD relevant experience * Demonstrated experience with one or more of the following domain-aware modeling paradigms: * + Physics-Informed Neural Networks (PINNs) * + Biology-Informed Neural Networks (BINNs) / Visible Neural Networks (VNNs) * + Neural Ordinary/Partial Differential Equations (Neural ODEs/PDEs) * + Operator learning methods (e.g., DeepONet, Fourier Neural Operator) * + Hybrid mechanistic-data-driven models * Experience with Bayesian inference, Gaussian processes, hierarchical models, or probabilistic programming. * Familiarity with nonlinear dynamics, dynamical systems theory, or systems biology modeling. * Background in surrogate modeling, model reduction, or multi-fidelity methods. * Exposure to genomics data structures (e.g., variant matrices, linkage disequilibrium, population genetics) or quantitative genetics (e.g., genomic BLUP, marker-effect models) - not required, but valued. * Experience deploying ML models into production environments (MLOps, containerization, cloud-based HPC). * Experience collaborating in interdisciplinary research teams spanning experimental and computational scientists. * Familiarity with ensemble methods, gradient-boosted models, kernel methods, or classical statistical learning as complementary tools. ## Description 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, conservation laws, symmetry properties, or known functional relationships into neural network training, ensuring 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. * Research Contribution: Contribute to publications in leading venues, participate in the internal scientific community, and stay at the frontier of scientific machine learning methodology. * Documentation & Communication: Prepare comprehensive technical documentation, present findings to both technical and non-technical stakeholders, and build organizational trust in AI-driven decision-making. WHO YOU ARE Bayer seeks an incumbent who possesses the following: Required: * PhD in one of the following or closely related fields ## Related Videos - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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