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Job description
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
Report findings to stakeholders and leadership in written reports and verbal presentations.
Qualifications
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
PhD in Computer Science, Applied Mathematics, quantitative disciplines with strong focus in ML, or related field
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
Preferred Qualifications
Experience using AWS services
Experience with machine learning integration in experiment workflows
Flagship Pioneering and our ecosystem companies are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
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Worksite address
cambridge, MA, 02140, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.