Applications for the Summer School for Systems Modeling will reopen 2025.

In this summer school, IIASA provides systematic guidance on the development and use of mathematical and computer models, addressing uncertainties in data and processes, exploring solution spaces, and distilling viable options for taking policy action. Our goal is to equip you with a solid understanding of modeling practices and limitations of models.

The two-week course is designed for Master’s and PhD students, as well as post-doctoral researchers who wish to use or develop models broadly related to the sustainability agenda. It is open to newcomers but also to those who have already been exposed to modeling and would like to add depth to their experience. It is also open to systems researchers who would like to gain a better understanding of the available tools. Many examples we will discuss are taken from IIASA’s portfolio of tools and methods, ranging from ecological models, socioeconomic systems, to integrated assessment tools of energy, air, water, biodiversity, and food.


The summer school proceeds in three broad phases:

Phase 1: General motivation of systems perspectives; an introduction to complex models for decision support; good modeling practices.
(Lectures and practice, Days 1-2 of Week 1)

Phase 2: Elective, parallel modules on specific modeling approaches and tools; avoidable pitfalls
(Lectures and practice, Days 3-5 of Week 1)

Phase 3: Team project development phase (Team work, Week 2). As a member of a team you will design and develop a tool for addressing a challenge related to the sustainability agenda.  

The school concludes with a round of presentations by participants. The emphasis of the school lies on the concepts and applications of methods, the art-and-craft of modeling, and an ambitious project phase. The team activities during Week 2 may develop into future research collaborations between you and other participants which we highly encourage.

After this course, you will not only be able to design and implement models, but also to critically assess modeling approaches of others. To benefit most from this course, you should feel comfortable with quantitative methods, including differential equations, optimization, and basic probability calculus, as well as with reading and writing simple code (e.g. in Python or R). In the selection process your specific academic field is less important to us than your enthusiasm, curiosity, and willingness to work in international and interdisciplinary teams.

Application information