Co-op, Machine Learning for Digital Twins

Contribute to ML models for scientific and experimental systems focused on a well-defined digital twin sub-problem. Build and train surrogate, operator-learning, or physics-informed models against experimental and simulation data with mentor guidance. Calibrate models, quantify uncertainty, and validate against data from active AISF campaigns. Frame open-ended scientific questions as concrete ML tasks with clear datasets, baselines, and evaluation criteria. Document findings and share results in cross-departmental collaboration through write-ups and presentations. Bonus points for experience with modern operator-learning methods and digital twins.