Co-op in Machine Learning for Digital Twins (Master’s/PhD) at Lila Sciences Lila Sciences in United States

Co-op opportunity in Machine Learning for Digital Twins at Lila Sciences

This post advertises a co-op opening in Machine Learning for Digital Twins at Lila Sciences in Cambridge, Massachusetts, United States. The role is aimed at a Master’s or PhD student with strong interest in applying machine learning to scientific and experimental systems.

The work sits within Physical Sciences AI and focuses on building calibrated, uncertainty-aware digital twins that support the design and execution of next-generation AI Science Facility experiments. The research and engineering themes include surrogate modeling, operator learning, physics-informed machine learning, spatiotemporal modeling, model calibration, uncertainty quantification, and validation.

Students who are excited by scientific machine learning, neural operators, Bayesian optimization, active learning, and real-world physical science applications are especially encouraged to apply. The role also involves working with experimental and simulation data from active scientific campaigns and translating open-ended scientific questions into concrete ML tasks.

Eligibility highlights: Master’s or PhD student; interest in ML for science and engineering; familiarity with scientific ML concepts is highly relevant. No deadline, funding package, or formal application portal is specified in the post.

How to apply: Review the LinkedIn post and follow the application instructions there. The poster also invites interested candidates to reach out directly.