I hold a PhD in theoretical solid state physics (density functional perturbation theory and ab initio molecular dynamics applied to phase transition problems). Nowadays I work at the interface between physics and machine learning, where projects cover ML surrogate models for complex simulations, uncertainty quantification methods to make neural network models more trustworthy as well as other Bayesian methods such as Gaussian processes.