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HGS MathComp - Where Methods Meet Applications

The Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences (HGS MathComp) at Heidelberg University is the only graduate school in Germany to focus on the complex topic of Scientific Computing. Placed in a vibrating research environment, the school offers a uniquely structured interdisciplinary education for PhD students. The program enables students to pursue innovative PhD projects with a strong application-oriented focus anywhere from mathematics, physics and chemical engineering sciences to cultural heritage.

Members of HGS MathComp are top experts in their fields and work on projects that combine mathematical methodology with topical research issues. Individual mentoring and career-building programs ensure that graduates acquire all qualifications for top positions in industry and science.

Upcoming Events [see all...]

9:00 - 13:00
Key Competences
Effective Visual Communication of Science
Location: Online
Registration: Please register on the event website
Organizer: Graduate Academy
This course is part of the course program of the Graduate Academy.

The latest information and a registration link are available on the course website (log in with Uni-ID).

HGS MathComp fellows can get a reimbursement of the course fees. Please submit your proof of payment and certificate of participation to

You will learn to visually communicate your complex research ideas and results so your messages are effortlessly understood by any specific audience (scientists or non-scientists). We will not focus on aesthetics but on how understanding human visual perception can inform your design decision for better comprehension of your scientific images, posters, and slides. You will also design a graphical abstract of your research, discuss it with peer scientists in a group exercise, and get actionable advice and feedback on your own materials. It is an immersive workshop, comprehensive, structured, memorable, easy to follow, useful and fun. More at

Content & Method:

The training is offered as blended learning that combines a self-study module and a live online workshop. All participants get 12 month access to all materials.
9:30 - 13:00
Key Competences
Introduction to Python Testing
Compact Courses
Speaker: Dr. Liam Keegan, Research Software Engineer, Scientific Software Center (SSC)
Location: Mathematikon • Seminar Room 10, 5th Floor • Im Neuenheimer Feld 205, 69120 Heidelberg
Registration: Please register via this form
Organizer: Scientific Software Center (SSC)
This compact course is part of the course program of the Scientific Software Center (SSC) at Heidelberg University.

The latest information and a registration link are available on the course website.


Basic Python knowledge and a laptop is required. Experience with writing tests is not required.


An automated test suite makes it much easier to maintain, extend and debug your Python code. In this course we will learn how to write automated tests in Python using the pytest library. After introducing the key concepts, the majority of the course will be hands-on, writing and running tests.
Learning Objectives

After the course participants should be able to:

- Install and run pytest
- Write simple tests
- Use temporary files in tests
- Use fixtures to manage resources
- Parametrize tests
- Add an automated test suite to their existing python projects
16.10.2023 - 20.10.2023
Practicals & Schools
4EU+ Summer School: Modeling and Statistical Analysis of Extremes in Time Series (Copenhagen)
Location: Copenhagen, Denmark
Registration: Please register on the event website
Organizer: University of Copenhagen
The course aims at PhD and advanced Master's students in statistics, probability theory, and econometrics, or with a background in the aforementioned areas such as physics, and geosciences.

This summer school is part of Flagship 3 of the 4EU+ European University Alliance, a close partner of HGS MathComp. Please contact us for funding options through 4EU+ and HGS MathComp.

The understanding of the appearance of extremes in real-life time series (such as weather and climate observations, returns of stock prices, exchange rates, and stock indices, insurance claim data, failures in energy and social networks) requires suitable probabilistic models and their statistical analyses. Over the last 15-20 years such models and statistical tools have been developed under the assumption of serial dependence. They supplement classical extreme value analysis which deals with independent data.

The goals of the course are

- to introduce and discuss the recent developments of extreme value theory in the time series context. The main focus will be on heavy-tail phenomena, where extremes are particularly severe, and clustering effects when extremes appear in clumps, 

- to provide suitable statistical tools for analyzing the aforementioned phenomena,

- to provide relevant knowledge to graduate students about extreme behavior of random systems in contrast to their average behavior, 

- to learn about applications of extreme value theory from top experts in the field.