Sofia Kantorovich received her Master’s Diploma in Applied Mathematics from Ural State University, Ekaterinburg, Russia, in 2003, and her PhD in the Physics of Magnetism from Ural State University and Lomonosov Moscow State University in 2004. Her doctoral research focused on theoretical approaches to many-body systems of self-assembling magnetic nanoparticles.

From 2004 to 2007, she worked as a postdoctoral researcher at the Max Planck Institute for Polymer Research in Mainz, Germany, where she was engaged in the fields of statistical physics and computer simulations of soft matter systems, including polymers and colloids. She then continued her research at the University of Stuttgart, first as a postdoc and later as a senior Humboldt Fellow at the Institute for Computational Physics. Her work there centred on developing coarse-grained models and simulation methods for magnetic colloidal systems and ferrofluids, contributing to a deeper understanding of dipolar interactions, self-assembly, and structural transitions.

In 2012, she moved to the University of Rome “La Sapienza”, where, in addition to studying dipolar systems, she worked on computational and theoretical models of DNA self-assembling duplexes.

In 2013, Dr Kantorovich was appointed Associate Professor at the University of Vienna, where she leads a research group in Computational Dipolar Soft Matter Physics. Her research focuses on magneto-responsive materials, such as magnetic elastomers and ferrogels—composite systems comprising magnetic particles embedded in elastic matrices. These materials exhibit tunable mechanical properties under external magnetic fields and hold promise for applications in soft robotics, sensing, and biomedical engineering.

Her approach combines theoretical modelling, continuum mechanics, and advanced simulations to understand how microscopic interactions influence macroscopic material behaviour. In this context, she has introduced novel concepts to describe anisotropy, plasticity, and magneto-mechanical coupling in soft magnetic systems.

Her current research seeks to integrate multi-scale simulations, statistical mechanics and machine learning  for the in silico design of adaptive magnetic soft materials with programmable functionality.