Sr. Machine Learning Researcher, Domain-Aware Modeling & Scientific Machine Learning
Residence Based, Residence Based, US
USD 120K-170K Senior-level Full Time
Tasks
- Collaborate with domain experts to validate model outputs
- Contribute to scientific publications
- Create technical documentation and stakeholder presentations
- Deploy models into production using breeding and discovery pipelines
- Design domain aware machine learning models
- Develop neural network architectures and loss functions with scientific constraints
- Develop probabilistic surrogate models
- Identify causal genetic variants
- Implement uncertainty quantification with Bayesian deep learning
- Implement uncertainty quantification with ensemble methods
- Predict complex traits from genomic phenomic and environmental data
- Prioritize genome editing targets with quantified uncertainty
Perks/Benefits
Skills/Tech-stack
Applied Mathematics | Bayesian Inference | Cloud Computing | Containerization | Data Analysis | Deep learning | Differential Equations | Dynamical systems | Ensemble Methods | Ensemble learning | Fidelity modeling | Gaussian Process | Gaussian Processes | Gaussian process regression | Genomic Data Analysis | Genomic data | Genomics | HPC | JAX | MLOps | Machine Learning | Model reduction | Multi Fidelity Modeling | Neural Networks | Neural Ordinary Differential Equations | Neural Partial Differential Equations | Ordinary differential equations | Partial differential equations | Physics Informed Neural Networks | Probabilistic Modeling | Probabilistic Programming | PyTorch | Scientific Computing | Surrogate modeling | TensorFlow | Uncertainty Quantification
Education
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