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.
News & Current Opportunities
Guest Program
Call for proposals for the Romberg Visiting Professor and Romberg Visiting Scholar 2026
Deadline: June 15, 2025
News
HGS MathComp Fellow Speaker Anna Lena Schaible wins young RSE prize at the deRSE25 in Karlsruhe.
April 7, 2024
Workshop
Integrative Think Tank (ITT) 2025 Heidelberg
With partner companies INOVA+ & SAP
Deadline: May 3, 2025
School
4EU+ Summer School on Machine Learning in Physics: Between Models and Reality
Deadline: May 1, 2025
Workshop
2nd Sorbonne-Heidelberg Workshop on AI in Medicine: Machine Learning for Multi-Modal Data
Deadline: May 2, 2025
24.04.2025
16:30 - 18:00
16:30 - 18:00
Theory & Methods
"Machine learning galore!" Lab Presentation & Science Talks
Colloquium
Location: Mathematikon • 5th Floor • Im Neuenheimer Feld 205 • 69120 Heidelberg
Registration: Please register via this form
Organizer: MLAI
Registration: Please register via this form
Organizer: MLAI
ECTS: 1 for 5
Machine learning galore will feature lab presentations by PIs as well as scientific talks by junior scientists.
To help plan the catering, please register for free by clicking here. (Deadline: April 22, 2024)
Scientific Machine Learning is a joint initiative from STRUCTURES and IWR aimed at fostering interactions within and development of the local machine learning community. Its portal summarizes the many relevant events and news from across campus that would otherwise remain scattered across single institutions or fields. The goals of the MLAI platform align with the STRUCTURES Cluster of Excellence's objective of driving research into the fundamental understanding of current and future machine learning, and with IWR’s aim to leverage machine learning to enable the solution of long-standing problems in the natural and life sciences, the engineering sciences, as well as the humanities.
Further information and links:
MLAI homepage • Machine Learning Talks on Campus – Information service and mailing list • STRUCTURES Cluster of Excellence
To help plan the catering, please register for free by clicking here. (Deadline: April 22, 2024)
Scientific Machine Learning is a joint initiative from STRUCTURES and IWR aimed at fostering interactions within and development of the local machine learning community. Its portal summarizes the many relevant events and news from across campus that would otherwise remain scattered across single institutions or fields. The goals of the MLAI platform align with the STRUCTURES Cluster of Excellence's objective of driving research into the fundamental understanding of current and future machine learning, and with IWR’s aim to leverage machine learning to enable the solution of long-standing problems in the natural and life sciences, the engineering sciences, as well as the humanities.
Further information and links:
MLAI homepage • Machine Learning Talks on Campus – Information service and mailing list • STRUCTURES Cluster of Excellence
Lab presentations:
Stephanie Hansmann-Menzeme • Jan Stühmer • Frank Zöllner
Rocket science:
Christoph Langenbruch (Hansmann-Menzemer lab)
Flavourful Machine Learning at LHCb
Leif Seute (Stühmer lab)
Generative Machine Learning for the Design of Dynamical Proteins
Anika Strittmatter (Zöllner lab)
Multimodal Medical Image Registration Using Deep Learning
Stephanie Hansmann-Menzeme • Jan Stühmer • Frank Zöllner
Rocket science:
Christoph Langenbruch (Hansmann-Menzemer lab)
Flavourful Machine Learning at LHCb
Leif Seute (Stühmer lab)
Generative Machine Learning for the Design of Dynamical Proteins
Anika Strittmatter (Zöllner lab)
Multimodal Medical Image Registration Using Deep Learning
05.05.2025
- 08.05.2025
09:00 - 13:00
09:00 - 13:00
Theory & Methods
Romberg Course: Krylov Subspace Methods - Mechanics, Convergence, and Advanced Topics
Compact Courses
Speaker: Prof. Kirk M. Soodhalter • The University of Dublin • Romberg Visiting Scholar
Location: Mathematikon • Seminar Room 10 & Conference Room, 5th floor • Im Neuenheimer Feld 205, 69120 Heidelberg
Registration: Please register here • Registration open until April 30, 2025
Organizer: HGS MathComp
Location: Mathematikon • Seminar Room 10 & Conference Room, 5th floor • Im Neuenheimer Feld 205, 69120 Heidelberg
Registration: Please register here • Registration open until April 30, 2025
Organizer: HGS MathComp
ECTS: 1
This course is part of the HGS MathComp Romberg Program.
Target audience: this course will be taught at the beginner/intermediate level and is targeted at graduates students in applied mathematics and cognate disciplines who work with and use iterative solvers for large scale linear (and at times non-linear) problems. These topics may also be of interest to advanced mathematics students. Reading materials will be made available for those who want to read further or who need to do some background reading. Students attending should have some background in linear algebra (theoretical and computational) and some basic programming knowledge in Matlab or Python.
Exercises: Lectures will be accompanied by some hands-on tutorials/problem solving exercises. The lecturer will present computational examples in Matlab, but it should not be a problem to follow along in Python for students who prefer that language.
The course will be held in Seminar Room 10 on May 5, and in the Conference Room from May 6-8.
Target audience: this course will be taught at the beginner/intermediate level and is targeted at graduates students in applied mathematics and cognate disciplines who work with and use iterative solvers for large scale linear (and at times non-linear) problems. These topics may also be of interest to advanced mathematics students. Reading materials will be made available for those who want to read further or who need to do some background reading. Students attending should have some background in linear algebra (theoretical and computational) and some basic programming knowledge in Matlab or Python.
Exercises: Lectures will be accompanied by some hands-on tutorials/problem solving exercises. The lecturer will present computational examples in Matlab, but it should not be a problem to follow along in Python for students who prefer that language.
The course will be held in Seminar Room 10 on May 5, and in the Conference Room from May 6-8.
Krylov subspace methods are the workhorse methods for treating large-scale sparse linear systems arising in computational sciences, engineering, and industrial applications. The goal of this comprehensive short course is to introduce the students to the basic mechanics of Krylov subspace iterative methods as well as basic convergence theory, as an avenue to then present more advanced topics. There is a rich theory of convergence of these methods, including a number of open questions. This is a topic of interest in its own right, but building a good foundation will motivate later lectures on specialty topics. The course consists of seven lectures spread over four half-days (5-8 May):
Lectures 1-2: Projection-based iteration and Krylov subspaces
Lectures 3-4: Subspace Recycling
Lectures 5-6: Block Krylov Subspace Methods
Lecture 7: Parallelism and high-performance computing
Detailed course description: https://heibox.uni-heidelberg.de/f/f28aa7cc8a62496c8767/
Lectures 1-2: Projection-based iteration and Krylov subspaces
Lectures 3-4: Subspace Recycling
Lectures 5-6: Block Krylov Subspace Methods
Lecture 7: Parallelism and high-performance computing
Detailed course description: https://heibox.uni-heidelberg.de/f/f28aa7cc8a62496c8767/
08.05.2025
- 22.05.2025
13:00 - 17:00
13:00 - 17:00
Key Competences
Introduction to programming with Python
Compact Courses
Speaker: Boyana Boneva (codeprehensible) & Kevin Leiss (codeprehensible)
Location: Online
Registration: Please register on the event website
Organizer: Graduate Academy
Location: Online
Registration: Please register on the event website
Organizer: Graduate Academy
ECTS: 2
This course is part of the course program of the Graduate Academy. Please note that this course will be held in English.
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 hgs@iwr.uni-heidelberg.de.
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 hgs@iwr.uni-heidelberg.de.
This course introduces the general-purpose programming language Python, which is used by web developers, data scientists and machine learning experts. Understanding the basics of Python will allow you to grasp the concepts of tools you might encounter and quickly apply them to your own research.
Within the scope of this course, you will master the Python philosophy, syntax, and writing your own scripts and modules. In addition, you will use your newly acquired skills to perform hands-on exercises and learn to conduct reproducible in-silico research. After completing this course, you will be ready to start your own Python journey and delve deeper into the world of Data Science.
Requirements: No previous programming knowledge is required! We will use our own server platform for the course, therefore no additional installation of software is needed.
Within the scope of this course, you will master the Python philosophy, syntax, and writing your own scripts and modules. In addition, you will use your newly acquired skills to perform hands-on exercises and learn to conduct reproducible in-silico research. After completing this course, you will be ready to start your own Python journey and delve deeper into the world of Data Science.
Requirements: No previous programming knowledge is required! We will use our own server platform for the course, therefore no additional installation of software is needed.