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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 one of the leading graduate schools in Germany focusing on the complex topic of Scientific Computing. Located in a vibrant research environment, the school offers a structured interdisciplinary education for PhD students. The program supports students in pursuing innovative PhD projects with a strong application-oriented focus, ranging from mathematics, computer science, bio/life-sciences, physics, and chemical engineering sciences to cultural heritage. A strong focus is put on the mathematical and computational foundations: the theoretical underpinnings and computational abstraction and conception.

HGS MathComp Principal Investigators are leading experts in their fields, working on projects that combine mathematical and computational methodology with topical research issues. Individual mentoring for PhD candidates and career development programs ensure that graduates are fully equipped to take up top positions in industry and academia.

Upcoming Events [see all...]

09.04.2026
10:00 - 12:00
Key Competences
Research data: Should it be open, and under what conditions?
Seminar

Speaker: Carolina Manfredini (University of Milan, Italy) • Sebastian Zangerle (University of Heidelberg, Germany)
Location: Online
Registration: Please register on the event website
Organizer: 4EU+ European University Alliance
ECTS: not yet determined
The movement for open science is transforming the academic world. In response, our 4EU+ training programme meets the growing demand for transparency, reproducibility, and collaboration by exploring a broad range of practices, such as open peer review, FAIR and open data, open software, and citizen science. In 2026, we invite you to explore the basics of open science in our introductory webinars followed by six specialized workshops between March and June.

ECTS subject to overall workload completed within the workshop series (please provide certificates after the program).

In the open science movement, research data is expected to be as open as possible and as closed as necessary. But what does this mean in practice, especially when dealing with patents, personal data or other sensitive information? This lecture will present the principles of open and FAIR data, research data management and the legal, ethical and institutional frameworks that govern data sharing. Participants will gain an overview of researchers’ rights and obligations and funders’ requirements, as well as guidance on data repositories.
 
09.04.2026
17:30
Theory & Methods
Hans Jensen Lecture: Centaur Science. Adventures in AI and Physics
Talk

Speaker: Prof. Dr. Jesse Thaler • MIT, Insitute for Artificial Intelligence and Fundamental Interactions
Location: Physikalisches Institut • Hörsaal 1 • Im Neuenheimer Feld 308, 69120 Heidelberg
ECTS: 0
The mythical centaur (half human, half horse) has become a metaphor for human-AI collaboration. In this talk, Prof. Thaler explores what centaur science looks like at the intersection of artificial intelligence and fundamental physics. He shares adventures from both directions of this exchange: teaching machines to “think like a physicist” by incorporating physics principles into machine learning frameworks, and teaching physicists to “think like a machine” to maximize discovery opportunities in both experimental and theoretical physics.
 
14.04.2026
13:00 - 14:00
Theory & Methods
Learning for X-ray Computed Tomography
Talk

Speaker: Dr. Felix Lucka • Centrum Wiskunde & Informatica, Netherlands
Location: Mathematikon • Conference Room, Room 5/104, 5th Floor • Im Neuenheimer Feld 205, 69120 Heidelberg
ECTS: 0
Due to its remarkable success for a variety of complex image processing problems, Artificial intelligence (AI), in partciular Deep Learning (DL), is nowadays also more commonly used in the field of X-ray Computed Tomography (CT). In this talk, we will highlight some of the challenges and potential solutions of integrating Deep Learning into tomographic work-flows found in scientific, clinical or industrial applications. In particular, we will cover the acquisition of large-scale experimental data collections suitable for DL, the development of benchmarking frameworks for image reconstruction methods, adaptive projection angle selection using reinforcement learning and the use of diffusion models for tomographic image reconstruction.