Research pillars


IFISC is a prime example of a cohesive and integrated working environment, which is a distinctive asset that sets it apart as a leading Complex Systems Institute on a global level. Unlike other research organizations, IFISC's research is structured around research lines rather than research groups, resulting in a seamless exchange of ideas and collaboration across different lines.

The María de Maeztu project is designed to leverage this collaborative approach by focusing on three fundamental pillars within the general line of Information Processing in Complex Systems: adaptation and learning, and emerging collective effects. This will help to drive IFISC's scientific staff in the pursuit of groundbreaking research during the project.


Figure illustrating the bidirectional interaction between the three research pillars (RP), being within the general line for Information Processing in Complex Systems.


The field of Complex Systems has gained significant attention in recent years due to its relevance across various fields, including social systems. At the heart of this field lies the study of emergent phenomena and the Micro-Macro paradigm. In this context, Information and Communication Technologies (ICT) are considered a prototypical socio-technical system that drives the majority of societal developments and challenges faced today. To further our understanding of collective effects in social systems, we leverage our expertise in modeling socio-technical complex systems.

We pursue this goal by deploying digital twin technologies, which offer open platforms for realistic data-driven social simulations. Our approach is focused on gaining a deeper understanding of critical societal issues and developing innovative solutions to mitigate them. For example, we are exploring how to reduce pollution in cities through the development of novel transportation strategies, analyzing the robustness of renewable-energy power grids, and reducing delays in the air transport system.

Our overarching objective is to develop a comprehensive understanding of complex systems and apply this knowledge to address pressing societal challenges. 

The key concepts in this pillar are:

  • High-order interactions memory
  • Learning and co-evolution of social dynamics
  • Information ecosystems
  • Digital twins, mobility, energy and air transport


The emergence of digital computing has revolutionized modern society, but the high energy consumption and limitations of traditional digital computing hardware present challenges in dealing with cognitive tasks. To overcome these issues, our research pillar focuses on exploring unconventional computing concepts that diverge from current digital computing standards.

Our specific interest is in bridging brain-inspired computing with machine learning and other computing-related fields, utilizing classical and quantum dynamical systems and complexity science as connecting hubs. We aim to identify fundamental computing concepts, simplify them to their minimal requirements, and optimize them for various constraints.

Our ultimate goal is to advance the field of unconventional computing and develop innovative approaches that address the challenges of traditional computing. Through our efforts, we hope to pave the way for energy-efficient and cognitively capable computing technologies that will have a significant impact on modern society.

The key concepts in this pillar are:

  • Brain-inspired computing
  • Photonic and optoelectronic implementations of brain and neuromorphic computing
  • Quantum physical machine learning
  • Quantum computation at nanoscale
  • Fundamental aspects and interpretability of Machine Learning models
  • Applications of Machine Learning


Information processing, adaptation, and learning are essential components of the functionality of biological systems, from genetic and neuronal circuits to populations of bacteria and viruses. These processes play a critical role in the mechanisms of evolution, as well as the ability of biological systems to respond to external stimuli and changes.

At a larger scale, understanding the impact of adaptation and information processing is crucial in addressing challenges in biodiversity, population health, resilience, and global change. Our research pillar aims to explore the principles of information processing, adaptation, and learning in biological systems to gain insights into their underlying mechanisms.

We focus on investigating the connections between these principles and large-scale systems, including interactions between species in ecology and climate systems. By doing so, we hope to develop a deeper understanding of the complex interplay between biological processes and the environment. Ultimately, our goal is to leverage these insights to develop innovative solutions for biodiversity conservation, population health, and climate resilience.

The key concepts in this pillar are:

  • Microscopic: gene networks, virus and bacterial population dynamics, neuronal circuits
  • Mesoscopic: epidemics, ecosystem resilience
  • Planetary-scale: network-based oceanic flow, climatic variability, atmospheric-oceanic phenomena


This web uses cookies for data collection with a statistical purpose. If you continue browsing, it means acceptance of the installation of the same.

More info I agree