This course is the basic introduction to modern cosmology. It introduces students to the main concepts and formalism of cosmology, the observational status of Hot Big Bang theory
and discusses major physical processes in the early Universe. ...
This course presents the physical principles and the recent research developments on three topics of particle and nuclear physics: the physics of neutrinos, dark matter, and plasmas of quarks and gluons. An emphasis is given on experimental aspects in thes ...
This course aims to make students familiar and comfortable with the main concepts of particle physics, providing a clear connection between the theory and relevant experimental results, including the most recent ones from modern particle physics experiment ...
We provide a comprehensive overview of astroparticle physics, bridging the microworld of cosmic messengers with the large-scale structure and evolution of the Universe. We also explore the detection techniques and technologies that make this investigation ...
This studio aims for designing interventions, programs, spaces; and proposing modes of collectivity and instituting for the inhabitation of human and more than human agencies in sites across Switzerland, where water cycles entangle with post Anthropocene l ...
The dynamics of ordinary matter in the Universe follows the laws of (magneto)hydrodynamics. In this course, the system of equations that describes astrophysical fluids will be discussed on the basis of selected astrophysical examples, from the physics of s ...
The course will cover some fundamentals of magnetohydrodynamics (MHD) theory with hands-on sessions to learn the basics of MHD simulations using the open-source Pencil Code (http://pencil-code.nordita.org/). ...
This is a PhD course on recursive methods used in modern macroeconomics. Recursive
representations of macroeconomic models are useful because they are parsimonious and allow
the computer to be used to solve for the equilibrium and the dynamics of the model ...
This course offers an overview of direct data-driven approaches to control design. In such methods, learning tools are used to compute optimal control laws from data without relying on a model of the system. Pros and cons of direct approaches as compared t ...