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.
Current Opportunities
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
07.04.2025
- 11.04.2025
10:00 - 14:30
10:00 - 14:30
Theory & Methods
Romberg Course: Quantifying extreme events in uncertain systems
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Compact Courses
Speaker: Prof. Georg Stadler • New York University • Romberg Visiting Scholar
Location: Mathematikon • Seminar Room 9, 4th floor • Im Neuenheimer Feld 205, 69120 Heidelberg
Registration: Please register here • Registration open until April 3, 2025
Organizer: HGS MathComp
Location: Mathematikon • Seminar Room 9, 4th floor • Im Neuenheimer Feld 205, 69120 Heidelberg
Registration: Please register here • Registration open until April 3, 2025
Organizer: HGS MathComp
ECTS: 2
This course is part of the HGS MathComp Romberg Program.
The course will consist of 90-minute theory sessions in the morning (10-12) and practical sessions on Tuesday and Thursday afternoon (1-2:30).
In systems with uncertain parameters or white noise forcing, one often focuses on low-order statistics such as means and variances. However, when rare/extreme events have severe consequences-such as hurricanes, energy grid blackouts, or building collapses-estimating tail probabilities becomes crucial. This mini-course will first discuss the challenges of rare event probability estimation. It will then introduce key methods for exploring tail probabilities, including importance sampling, subset simulation, and optimization-based techniques for identifying dominating points in rare event sets.
Prerequisites: Basic undergraduate Probability, Optimization and Analysis.
The course will consist of 90-minute theory sessions in the morning (10-12) and practical sessions on Tuesday and Thursday afternoon (1-2:30).
In systems with uncertain parameters or white noise forcing, one often focuses on low-order statistics such as means and variances. However, when rare/extreme events have severe consequences-such as hurricanes, energy grid blackouts, or building collapses-estimating tail probabilities becomes crucial. This mini-course will first discuss the challenges of rare event probability estimation. It will then introduce key methods for exploring tail probabilities, including importance sampling, subset simulation, and optimization-based techniques for identifying dominating points in rare event sets.
Prerequisites: Basic undergraduate Probability, Optimization and Analysis.
10.04.2025
09:00 - 13:00
09:00 - 13:00
Key Competences
Effective Visual Communication of Science
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Compact Courses
Speaker: Dr. Jernej Zupanc (Seyens)
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: 0.5
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.
Aim:
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 https://www.seyens.com
Contents & 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.
1. Self-study via an online platform (6-8 hours of engaging video content & a useful assignment):
1.1. Communicating with scientific vs non-scientific audiences
1.2. Visual perception and what humans find intuitive
1.3. Layout: simplifying comprehension through structured layout
1.4. Eye-flow: effortlessly guide the audience through the design
1.5. Colors: how to amplify, not ‘fancify’
1.6. Typography for legibility, structure and aesthetics
1.7. Digital images in science: the optimal use of vector and raster images
1.8. Slides that amplify messages and don't distract when presenting
1.9. Posters: strategy and process for creating posters that attract and explain
1.10. Homework: participants submit images and slides to the trainer to receive feedback
2. Live Online Workshop (April 10, 2025, 9 am – 1 pm via Zoom, interactive and hands-on)
2.1. Recap of fundamentals and Q&A: trainer facilitates an effective recap of lessons learned in self-study module and answers all further questions.
2.2. Exercises & group work: participants draw a graphical abstract of their research and share their posters and we form groups so everyone gives and receives informed feedback.
Discussion on pre-submitted materials: participants receive actionable suggestions on how to improve their own images and slides from the trainer and on posters from fellow researchers.
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 https://www.seyens.com
Contents & 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.
1. Self-study via an online platform (6-8 hours of engaging video content & a useful assignment):
1.1. Communicating with scientific vs non-scientific audiences
1.2. Visual perception and what humans find intuitive
1.3. Layout: simplifying comprehension through structured layout
1.4. Eye-flow: effortlessly guide the audience through the design
1.5. Colors: how to amplify, not ‘fancify’
1.6. Typography for legibility, structure and aesthetics
1.7. Digital images in science: the optimal use of vector and raster images
1.8. Slides that amplify messages and don't distract when presenting
1.9. Posters: strategy and process for creating posters that attract and explain
1.10. Homework: participants submit images and slides to the trainer to receive feedback
2. Live Online Workshop (April 10, 2025, 9 am – 1 pm via Zoom, interactive and hands-on)
2.1. Recap of fundamentals and Q&A: trainer facilitates an effective recap of lessons learned in self-study module and answers all further questions.
2.2. Exercises & group work: participants draw a graphical abstract of their research and share their posters and we form groups so everyone gives and receives informed feedback.
Discussion on pre-submitted materials: participants receive actionable suggestions on how to improve their own images and slides from the trainer and on posters from fellow researchers.
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
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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/