News

Ignasi Puch Giner defended his doctoral thesis, supervised by Víctor Guallar Tasies, at the North Campus on April 16, 2026. Titled “Optimization of Monte Carlo and Molecular Dynamics Techniques for Receptor–Ligand Binding Studies”, the thesis focuses on taking advantage of the theoretical framework of Monte Carlo methods to design a protocol applicable to the energetic characterization of ligand–protein interactions. In addition, these methodologies, combined with other computational techniques, have been applied in three collaborations oriented to real cases related to pathologies of the nervous system and the cardiac system.

Jordi Pera i Ferreruela defended his doctoral thesis co-directed by Joaquim Casulleras i Ambrós and Jordi Boronat Medico on April 17, 2026 at Campus Nord. The thesis, entitled Itinerant ferromagnetism and polarons in SU(N) Fermi gases, focuses on determining under which regimes Fermi gases become ferromagnetic and, if so, how they get there. On the other hand, it also includes a small exploration of the behavior of impurities in these gases.

PhD student Yating Zhou publishes her first research article in the prestigious Chemical Engineering Journal.

Antonio Blanco Casares defended his thesis co-directed by Oriol Lehmkuhl and Daniel Mira on May 8, 2026 at Campus Nord. The thesis is titled "A Numerical Framework for Solving Complex Flow Regimes with Continuous Galerkin Methods" and investigates how new advances in numerical methods improve the computational simulations of physical systems involving flow problems.

Arnau Jurado Romero defended his thesis supervised by Rossend Rey Oriol (UPC) and Carles Calero Borrallo (UB) on May 28th, 2026 at Campus Nord. The thesis, with the title "Self-Propulsion of Molecular Swimmers", demonstrates a propulsion mechanism for a small molecule, via all-atom molecular dynamics simulations. It also fully investigates the nature of the resulting propulsion, for both single self-propelling particles, and many body systems. It concludes by applying state-of-the-art machine learning techniques to thermophoresis in aqueous solution, a closely related problem to self-propelling molecular swimmers.