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

About the role

Scientific ML researcher designing domain-aware, interpretable models with scientific knowledge, structured and multimodal data, and uncertainty quantification. Candidate overlaps in graph ML, self-supervised learning, explainability, and structured-data modeling; agriculture and 5+ years post-PhD are the main gaps.

Requirements

• PhD in ML/deep learning, applied mathematics, computational science, or CS with scientific computing focus
• Research output in scientific ML, numerical methods, or data-driven physical/biological modeling
• PyTorch, JAX, or TensorFlow
• Experience with high-dimensional structured or multimodal data
• Preferred: 5+ years post-PhD relevant experience
• Preferred: domain-aware models such as PINNs, neural ODEs/PDEs, or hybrid mechanistic models

What you'll do

• Develop biology-informed or hybrid mechanistic-statistical ML models
• Design architectures and loss functions that encode scientific constraints
• Model genomic, phenomic, and environmental data
• Build uncertainty quantification frameworks
• Transition research prototypes into production-grade models

Where this was posted

• LinkedIn

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