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.
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
The Academy is aimed at Master students as well as PhD candidates.
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.
Location: Mathematikon • Im Neuenheimer Feld 205, 69120 Heidelberg
Registration: Please register on the event website • Registration open until June 15, 2026
Organizer: IWR
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.
Registration: Please register on the event website • Registration open until June 30
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.
- 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