Director(s): Martino Trassinelli, martino.trassinelli@insp.jussieu.fr; Fabio Finocchi, fabio@insp.jussieu.fr
Funding: Sorbonne Center of Artificial Intelligence (Sorbonne Université)
Description : https://w3.insp.upmc.fr/wp-content/uploads/2025/04/PhD_QNEST_2025.pdf
Start: 2025
End:
PhD Sudent :
Team(s): Clusters and Surfaces under Intense Excitation  Low-dimensional oxides  
Teams' Page of thesis : Clusters and Surfaces under Intense Excitation   Low-dimensional oxides  
Thesis status: Proposed thesis and Thesis

 

We propose an interdisciplinary project involving different fields such as Bayesian data analysis, statistical physics and applied mathematics for the efficient exploration and minimization of complex scalar functions. The project is centered on the Nested Sampling optimization algorithm based on Bayesian statistics to explore the relevant parameter space recursively effectively, obtain the topology thereof, and assess the quality and reliability of the obtained optimum within a pre-determined statistical error.

Detailed description here.