The company is looking to accelerate the transition to a sustainable economy by developing a novel AI and data-driven approach to materials discovery and development.
Requirements
- Experience with uncertainty quantification, active learning and Bayesian Optimization
- Experience implementing, evaluating, and hyperparameter tuning small and large supervised models in a Bayesian Optimization context (Gaussian processes, Bayesian Neural Networks) on small and large datasets
- Strong experience in at least one ML framework (PyTorch/TensorFlow/Jax) and robust experience in Python data science ecosystem (Numpy, SciPy, Pandas, etc.)
- Experience using a cloud computing service to reduce runtime to train and evaluate deep learning models
- Experience using AWS services
- Experience with machine learning integration in experiment workflows
Responsibilities
- Design, build and scale supervised ML models for active learning and Bayesian Optimization of materials synthesis and performance
- Implement best practices and innovate methods for uncertainty quantification
- Combine datasets of multiple fidelities and sources to power data-driven materials discovery
- Work with the computational team to identify materials design pathways that target desired functional properties and their synthesis
- Work with infrastructure and automation teams to transfer data and predictions in real time
- Work with the experimental team to drive material discovery and development, and build domain-specific acquisition functions
- Continually cultivate scientific/technical expertise through critical review of ML literature, attending conferences, and developing relationships with key opinion leaders
Other
- Strong self-starter and independent thinker, with strong attention to detail
- Demonstrated industry experience or academic achievement
- Excellent communication and presentation skills, capable of conveying technical information in a clear and thorough manner
- Eager to work with highly skilled and dynamic teams in a fast-paced, entrepreneurial, and technical setting
- PhD in Computer Science, Applied Mathematics, quantitative disciplines with strong focus in ML, or related field
