We build machine learning systems for the physical sciences: surrogate models for expensive simulations, learned controllers for experimental hardware, and inference pipelines that close the loop between measurement and design.
We are interested in working on: foundation models for scientific data, differentiable simulation, and the integration of AI into instrumentation and fabrication workflows.
Contact
nv@rknfpnyr.flfgrzf
