Applied ML Scientist - Chemistry
Tasks
- Adopt emerging machine learning methods
- Apply transfer learning across multiple data sources
- Collaborate on problem framing and optimization modeling
- Communicate methods results and limitations
- Curate and analyze chemical and experimental data
- Define domain of validity for downstream use
- Develop input representations for molecular and signal data
- Lead machine learning model development
- Quantify and calibrate model performance and uncertainty
- Select train and evaluate predictive models
Perks/Benefits
- N/A
Skills/Tech-stack
Active Learning | Agentic approaches | Bayesian optimization | Cheminformatics | Chemoinformatics | Data Pipelines | Domain Of Validity | Gaussian Process | LLM | Learned Embeddings | MLOps | Machine Learning | Model Deployment | Model calibration | Molecular descriptors | Molecular fingerprints | Neural Networks | Optimization pipelines | Physics Informed Neural Networks | Python | Regression | Spectral Features | Transfer Learning | Tree Ensemble | Uncertainty Quantification
Education
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