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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.

20.09.2026 - 25.09.2026
Practicals & Schools
Feel the Heat: Science and Histories of Fever
School

Speaker: Various Speakers
Location: Marsilius Kolleg of Heidelberg University • Im Neuenheimer Feld 130.1, 69120 Heidelberg
Registration: Please apply on the event website • Application open until 14 June 2026
Organizer: Marsilius Kolleg
ECTS: 3
The Marsilius Academy 2026 is a joint venture of Heidelberg University's Marsilius Kolleg with the Cluster of Excellence „SynthImmune“, and the ERC project „FEVER – Global Histories of (a) Disease, 1750-1840“. It is also co-sponsored by the German Center for Infection Research (DZIF) and the DFG Priority Programme “Physics of Parasitism” (SPP 2332).

The Academy is aimed at Master students as well as PhD candidates.

Fever has long been a threatening and inescapable feature of the human condition; as the German poet Friedrich Hebbel noted in 1860, “He who would live must hazard the fever.” For centuries, “fever” denoted a disease marked by various sensations – heat, but also an altered pulse, or delirium. Only from the 1840s onward was fever reduced to quantifiable temperature, and reframed as a symptom of diverse diseases, which from the late 1800s onward came to be associated with specific pathogens. Today, the re-emergence of old and the emergence of new infectious diseases rank among the principal challenges facing humankind. This multidisciplinary summer school brings together perspectives from the life and natural sciences as well as the humanities to re-examine the multifaceted nature of fever.

Topics include epidemics and pandemics of febrile illnesses; their prevention, including vaccines and their societal acceptance (or resistance); the sensory register of fever, including but exceeding heat and temperature, e.g. its production, theorisation, and effects on the body; disease causation, e.g. through pathogens and vectors; and febrile sequelae, post-acute infectious syndromes, and the narratives of sufferers unable to fully recover from febrile infectious diseases. Taken together, these approaches invite a renewed reckoning with fever as both a biological phenomenon and a social and cultural object.
 
21.09.2026 - 25.09.2026
Practicals & Schools
IWR School "AI for Science"
School

Speaker: Various Speakers
Location: Mathematikon • Im Neuenheimer Feld 205, 69120 Heidelberg
Registration: Please register on the event website • Registration open until June 15, 2026
Organizer: IWR
ECTS: 3
The summer school is designed for PhD students who want to leverage state-of-the-art AI in their research. Applicants may come from any scientific discipline, including physics, biology, medicine, neuroscience, and climate science.

The school offers a comprehensive overview of key topics in Artificial Intelligence and Machine Learning, including Generative AI, Explainability, Simulation-Based Inference, Agentic AI, Robustness and Validation of AI Methods, Vision-Language Models, Self-Supervised Learning, Knowledge Integration, and Causality. In addition to expert lectures, the program features hands-on sessions and best-practice sessions focused on how to use the latest AI tools in research.
Participants are expected to have a solid understanding of the core concepts of machine learning.

For more information, please visit the event website.
 
21.09.2026 - 25.09.2026
Practicals & Schools
KCDS Summer School 2026 on Graph Neural Networks
School

Location: KIT • Geb 20.30 • Englerstr. 2, 76131 Karlsruhe
Registration: Please register on the event website • Registration open until June 30
ECTS: 2
The program of this year's summer school will focus on Graph Neural Networks, covering both fundamental concepts and practical applications.

Participants can expect a mix of lectures and interactive sessions that provide insight into the mathematical foundations as well as real-world use cases of graph-based machine learning methods.

A tentative schedule will be released soon, where all relevant organizational details will also be provided.

The program includes:
- a poster session and networking dinner on the first day
- a social event
- hands-on tutorials with a focus on applications
- sessions combining theoretical perspectives with practical examples

Speakers:
- Jan Stühmer (HITS, KIT) – Introduction to GNNs, including a practical session, and Equivariance as Design Principle in Modern Machine Learning
- Ismail Ilkan Ceylan (TU Vienna, AITHYRA) – Graph Foundation Models
- Joel Oskarsson (ETH) – GNNs for Spatio-Temporal Modelling and Earth System Modelling
- Arghya Bhowmik (DTU) – GNNs for Material Sciences