-
unique opportunity to engage in transformational research that advances the development of AI-ready scientific data, optimized workflows, and distributed intelligence across the computing continuum. In
-
: The design and analysis of computational methods that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance
-
computing and network infrastructure. Ongoing upgrade projects include replacing legacy VME solutions with MicroTCA as well as developing next generation Accelerator Timing and Machine Protection Systems (MPS
-
experimental facilities. You will be responsible for developing energy-efficient, physics-aware algorithms designed for distributed learning across both high-performance and edge computing environments. You will