Rui Perdigão’s course on Complex System Dynamics, headquartered at the Meteoceanics Institute for Complex System Science, is now also available as a semester doctoral course at the University of Lisbon, offering 6 ECTS for students enrolled in partner programs.

The course is specially tailored to a wide-spectrum interdisciplinary audience spanning across natural, social, technical and exact sciences, and has already attracted bright doctoral and post-doctoral collaborators to Rui Perdigão’s professorial chair and institute.


  • Acquisition of fundamental competences in complexity sciences, their relevance and implementation in the conceptualization, systematization, modeling and formal analysis of the complex dynamics underlying climate change;
  • Learning fundamental principles that allow to formulate the dynamics of complex systems, including emergence of extreme phenomena, in an elegantly simple and effective way without loss of rigor nor generality;
  • Deepening scientific research, development and communication at the interface between natural and social frontier sciences.


  • Fundamental notions on the dynamics of complex systems, principles and underlying mechanisms in dynamic systems theory and physical information;
  • Methods of systematization of dynamic systems: simple conceptual structures representing complex natural, technical and social phenomena;
  • Fundamentals of the dynamics of the Earth system and the emergence of regimes, critical transitions and extreme events in the context of complexity sciences;
  • Coevolutionary models of climate change in a holistic perspective involving dynamics of the oceans, atmosphere, geosphere, biosphere and society;
  • Dynamic methods of extraction and analysis of information related to the dynamics of complex systems, from empirical and computational records;
  • Detection of patterns of spatial and temporal climatic variability from data of the dynamics of the Earth system and attribution to underlying mechanisms;
  • Methods of evaluating uncertainty and predictability in complex system dynamics, for representative model optimization and decision support.