Дополнительно
ML for imaging/inverse problems (or adjacent) with strong evaluation discipline and comfort with GPU performance constraints
Pragmatic production mindset: reproducible training/inference, regression testing, and safe deployment in high-stakes contexts
A background in computational physics or scientific computing
Leverage ML-based methods such as PiNNs and Neural Operators to solve partial differential equations arising in ultrasound simulation and imaging
Experience in Agentic-SciML is a plus
Hands-on experience with data curation for ML: building datasets from messy, real-world sources, defining ground truth, and managing labeling or simulation pipelines
Background in data assimilation: combining observations with physics-based models (Kalman filtering, variational methods, ensemble approaches, or learned variants)