Professor 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 Interuniversity Institute for Intelligence, Complexity and Security, offering 6 ECTS for students enrolled in programs from partner universities.
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 interuniversity chair and institutes.
- 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.