Location: Cambridge, MA, US
Job Summary:
Job Duties:
- Train, fine-tune, and deploy deep learning models for materials composition and performance.
- Develop and train architectures for representation learning and generative AI.
- Create physics-informed learning architectures and loss functions.
- Optimize machine learning models for materials synthesis and performance prediction.
- Contribute to a digital platform for continuous model improvement.
- Collaborate with experimental teams for material discovery.
- Present findings through reports, slide decks, and presentations.
Required Skills:
- Strong proficiency in PyTorch and deep learning model training.
- Expertise in integrating physics-based inductive bias in models.
- Proficient in Python and the data science ecosystem (NumPy, SciPy, Pandas).
- Excellent communication skills for diverse audiences.
Required Experiences:
- PhD in Computer Science, Applied Mathematics, or a quantitative discipline focused on machine learning.
- Proven track record in publishing scientific papers or contributing to public code bases in machine learning and materials science.
Job URLs: