We are seeking a Sr. Machine Learning Researcher with strongexpertisein 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 ingenomic selectionandgenome 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 andvalidatedomain-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 novelarchitecturesand lossfunctionsthat 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 thatleveragehigh-dimensional genomicphenomic and environmental data to predict complex trait outcomesidentifycausal 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 domainexpertiseinto model architecture decisions andvalidatemodel 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 venuesparticipatein the internal scientific community and stay at the frontier of scientific machine learningmethodology.

• 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 whopossessesthe following:

Required:

• PhDin one of the following or closely related fields:

• • 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 withdemonstrateddepth 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.

• Proficiencyin 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 communicationskills 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) / VisibleNeural Networks (VNNs)

• • Neural Ordinary/Partial Differential Equations (Neural ODEs/PDEs)

• • Operator learning methods (e.g.DeepONet Fourier Neural Operator)

• • Hybrid mechanisticdata-driven models

• Experience withBayesian inferenceGaussian processeshierarchical models orprobabilistic 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) - notrequired butvalued.

• 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.

Employees can expect to be paid a salary of approximately $120k-170k. Additional compensation may include a bonus or incentive program (if relevant).Additionalbenefits include health care vision dental retirement PTO sick leaveetc.. This salary (or salary range) is merely an estimate and may vary based on an applicants location market data/ranges an applicants skills andpriorrelevant experience certain degrees and certifications and other relevant factors.

This posting will be available for application until at least 6/26/26.