Quantitative methods and data-driven tools are evolving rapidly across disciplinary boundaries. As a result, it is becoming increasingly difficult to integrate current methodological developments into existing curricula timely and with sufficient depth. At the same time, interdisciplinary key methods and concepts are becoming increasingly important across many research fields, while often receiving only little attention in discipline-specific teaching.
Our Methodical Sprints are designed to meet these challenges. In compact block courses, they introduce current methods and interdisciplinary concepts – application-oriented, hands-on, and research-driven. They are aimed at students, doctoral researchers, and postdocs from STEM-related disciplines as well as from the quantitatively oriented humanities and social sciences.
The Methodical Sprints are conceived as an open format and bring together instructors from a wide range of disciplines. Interested in proposing a topic or offering a Methodical Sprint yourself? Feel free to get in touch.
© CDSC A short course on causal inference
Correlation is not Causation! But how can we find answers to questions like "How effective is a given treatment in preventing a disease" or "Did global warming cause this heat wave" based on available data? The scientific field of causal inference gives us tools to tackle these kind of questions. In this short course we will give a first introduction to causal thinking and its applications to problems from different scientific disciplines.
Lecturer: Dr. Oliver Kamps
19.-20.2.2026, KP 304, Institute for Theoretical Physics
A short course on critical (?) transitions
Abrupt transitions between qualitatively different states are a ubiquitous phenomenon in complex systems — from ecosystems and climate dynamics to psychological behavior and even large language models. Understanding, modeling, and anticipating such regime shifts is a major scientific challenge with wide-ranging applications.
In this methodical sprint, we provide a compact introduction to the key theoretical concepts behind critical transitions, including bifurcations, tipping points, and early-warning signals. We will then focus on practical methods for detecting and anticipating transitions directly from data. Finally, we address the critical question of how (if possible) to distinguish genuine critical transitions from other forms of sudden change — a distinction that carries important consequences for interpretation, prediction, and control.
Lecturer: Dr. Oliver Kamps
10.-11.2.2026, KP 304, Institute for Theoretical Physics
Synchronization and the Kuramoto Model
Synchronization is a ubiquitous phenomenon in nature and technology and a fundamental principle of collective dynamics. Synchronization processes play a central role not only in physics, but also in biology, chemistry, neuroscience, medicine, and engineering — for example in neuronal activity patterns, cardiac rhythms, the coordination of movement, circadian processes, chemical oscillations, swarm behavior, and the stability of electrical power grids.
The Kuramoto model (KM) is a paradigmatic model in nonlinear dynamics that, owing to its simple mathematical structure, enables a fundamental understanding of collective synchronization phenomena across a wide range of systems. This methodological sprint provides an introduction to synchronization and the KM. Topics include phase transitions and different dynamical regimes such as full and partial synchronization. In addition, selected examples illustrate how the model can be adapted to different application contexts and employed to investigate current scientific questions from various disciplines.
Target audience: students and researchers in physics, mathematics, computer science, biology, chemistry, medicine, psychology, sports science, and cognitive science.
Prerequisites: basic mathematical knowledge; additional concepts (e.g. oscillations and frequencies, fixed points, or related topics) will be introduced during the course or can be acquired through concise self-study based on tailored materials provided in advance.
Lecturer: Dr Katrin Schmietendorf
Next course dates: February 18–19, 2027.