Nicolas Rubido Obrer

Nicolas Rubido Obrer is a Lecturer in the Department of Physics at the School of Natural and Computing Sciences, University of Aberdeen, UK. He is affiliated with the Institute for Complex Systems and Mathematical Biology (ICSMB) and previously served as an Adjunct Professor at the Physics Institute, Universidad de la República (UdelaR), Uruguay, and as a Research Fellow at the Aberdeen Biomedical Imaging Centre (ABIC). He earned his PhD in Physics from the University of Aberdeen in 2014, with a thesis on energy transmission and synchronization in complex networks.

• PhD in Physics – University of Aberdeen (2014)
• MSc in Physics – Universidad de la República (2010)
• BSc in Physics – Universidad de la República (2008)

His research focuses on complex systems, employing network theory, dynamical systems, numerical modeling, and data analysis to study emergent behaviors such as synchronization and chaos. He applies these methods across interdisciplinary domains including brain networks (Alzheimer’s disease, sleep-wake cycles), climate modeling, and power-grid stability. He is particularly interested in network inference and reverse engineering system connectivity from observational data.

The recent trend in his publications reflects a strong emphasis on interdisciplinary neuroscience, with high-impact contributions in Nature Medicine, NeuroImage, and Scientific Reports, focusing on brain aging, neurodegeneration, and neuroimaging. His work also spans climate dynamics and foundational network theory, demonstrating both applied and theoretical depth.

His scientific awards include the prestigious Springer Theses Award (2015) and the Young Researchers Award at Dynamics Days Europe (2016), as well as the SUPA Studentship Prize (2011).

Nicolas supervises PhD and MSc students in Physics, Mathematics, and Applied Health Sciences, and is actively involved in international collaborations with institutions in Uruguay, Brazil, Spain, Argentina, and the UK. He has led and contributed to projects on EEG-based biomarkers for aging, functional ultrasound imaging, and climate forecasting. His research integrates theoretical modeling with experimental and clinical data, advancing methodologies in network science and complex systems analysis.

He is a member of the Non-Linear Physics group at UdelaR and associated with the Laboratory of Instabilities in Fluids. His ongoing work includes developing data-driven models for complex systems and exploring the role of network structure in synchronization and information transmission.