Saed obtained his PhD (2021) in nuclear engineering from Sejong University. He is experienced in Monte Carlo code systems such as MCNP and SCALE. His research interest is in nuclear core design and simulation. He quantified the reactivity change for the misloading event in the spent fuel cask and predicted the reactivity change with high accuracy using machine learning.
His current research is focused on the application of machine learning algorithms on neutron detectors with high-radiation tolerant to locate the fuel debris inside the Fukushima Daiichi nuclear power plant.
Saed has extensive experience in the nuclear industry and commissioning phases. He worked as a system and commissioning nuclear engineer in the Jordan Research and Training Reactor in the period 2012-2017.
