-
complex terrain regions. CMAS does this by innovating on the fronts of meteorological data acquisition, analysis, and interpretation (https://www.bnl.gov/cmas/). The CMAS work portfolio is conducted within
-
, enhanced by machine-learning and data-driven analysis techniques. Additionally, the study will encompass electrically triggered events that mimic the voltage-based signaling of biological synapses
-
development and is the nexus of a broad collaboration network. Each year, CFN staff members support the research of nearly 600 external facility users. Three strategic nanoscience themes underlie the CFN
-
rigorous wavefront simulations and AI/ML networks that account for the light-matter interactions in various wavelength regimes, and real light source parameters such as coherence, polarization
-
for scientific and security applications; (ii) ML model optimization for inference speed and deployment for real-time analysis; and (iii) AI model training for analog in-memory computing. The position provides