Baptiste Mouginot received his PhD from the University of Paris Sud-XI in 2011. During his PhD, he studied nuclear structure. During the first part of his PhD, he participated in study of the Giant Pairing Vibration, a collective excitation state predicted by many theoretical models but never observed. The second part was dedicated to half-life measurement in the Zn-74, allowing a better understanding of the shape deformation in the mass region. He joined the Experimental Research on Data Reactors and Energy (ERDRE) group of Subatech Laboratory (CNRS/IN2P3, University of Nantes, Ecoles des Mines de Nantes) in 2012 as a postdoc, then as a research scientist. He was involved in the study on Accelerator Driven System. Those results were included in the report given by the Commissariat à l’énergie atomique (CEA) to the French parliament in 2012. He started and led the development of a fuel cycle tool for the CNRS, called CLASS. He initiate the development of fuel fabrication model and cross section predictor for the CLASS tool, and has been a key developer of the polynomial regression and neural network methods for fuel fabrication models in CLASS. Mouginot joined the Cyclus development team at UW-Madison in October 2015.
- The nuclear fuel cycle,
- Physics models for nuclear fuel cycle,
- Machine learning (applied to nuclear reactor modeling),
- Nuclear reactor physics,
- Nuclear structure experiment.