I am an applied mathematician, and I work in mathematical methods for advance manufacturing under the umbrella of the Artificial Intelligent Manufacturing and Sustainability (AIMS) unit. I collaborate with engineers at the Aeronautics Advanced Manufacturing Center (CFAA, spanish acronym) in Zamudio.

I am interested in monitoring of metal cutting processes, which entails understanding the dynamics of metal cutting and modelling of machine elements performance. To a lower degree I am interested in FEM simulations.

Postdoc Fellow at BCAM. The main methodological research lines include probabilistic graphical models, supervised classification, information theory, density estimation and feature subset selection. The methodological contributions have been applied to the fields of bioinformatics (genetics and epigenetics) and ecological modelling (fisheries).

I am a Postdoctoral Fellow in the Modelling and Simulation in Life and Materials Sciences research group at BCAM. 

I obtained my PhD in Mathematics and Statistics in June 2025 from the Euskal Herriko Unibertsitatea (EHU) with a thesis entitled “Optimising performance of Hamiltonian Monte Carlo (HMC) in molecular simulation and computational statistics”, supervised by Prof. Elena Akhmatskaya (BCAM) and Prof. Jesús María Sanz-Serna (Universidad Carlos III de Madrid). My doctoral research was funded by the La Caixa – INPhINIT 2020 Fellowship.

My research focuses on the particle-based multiscale simulation of synthetic and biological soft matter, such as hierarchical assembly block copolymer and proteins, and the flow of colloidal and cellular systems.

Postdoctoral Fellow at BCAM. 

Research Technician at BCAM

BCAM-TECNALIA PostDoc Fellow

I am a Juan de la Cierva postdoctoral research fellow at BCAM, specializing in Modelling and Simulation in Life and Material Sciences. My research career is driven by the ambition to harness advanced materials simulation methods, including Density Functional Theory (DFT), ab initio Molecular Dynamics (AIMD), Classical Molecular Dynamics (CMD), and Machine Learning, to address challenges in materials science.

Veronica received the degree in Mathematics from the University of Salamanca, Spain, in 2019. She is at the Basque Center for Applied Mathematics-BCAM since July 2019 where she is developing data science techniques for energy applications. In particular, she is currently working on probabilistic predictions of energy consumption, generation, and price.