Newsroom
Stay informed with our latest news and announcements on this page. For more in-depth content, we also encourage visitors to explore our bimonthly STRUCTURES Newsletter magazine, which features a variety of articles, interviews with members, and background information on our latest research and activities.
We are pleased to announce the 2026 Les Houches Summer School on Quantum Theory on All Scales, taking place August 03-28, 2026 in the idyllic location of Les Houches in the French Alps. The school, which is supported by STRUCTURES, aims to highlight recent significant progress on the mathematical analysis of complex quantum systems, and to discuss interesting open questions for the future. Six lectures, along with numerous short courses and talks, will focus on interacting and correlated systems, as well as random systems – with methods drawn from analysis and probability to algebra and topology.
The main topics of the school will be:
- Topological Quantum Matter
- Open Quantum Systems
- Integral Representations for Quantum Theory and the Renormalization Group
- Quantum Physics and Randomness
- Macroscopic Quantum Systems: Beyond Mean-Field Descriptions
- Entanglement, Entropy and Spacetime
The last Les Houches school with this focus took place in 2010 under the title Quantum theory from small to large scales. It brought together many of the best doctoral students and postdoctoral researchers in the field and from all over the world and gave them a perspective beyond their specific thesis and research work. It further led to lasting research connections, friendships and a sense of community. Many of those that attended the 2010 school as PhD students and postdoctoral fellows have since been appointed as faculty at major research universities.
The summer school is organized by Sven Bachmann (University of British Columbia, Canada), Serena Cenatiempo (Gran Sasso Science Institute, L’Aquila, Italy), Alain Joye (Université Grenoble Alpes, Institut Fourier, France) and Manfred Salmhofer ( STRUCTURES, Universität Heidelberg, Germany).
Les Houches School of Physics is proud to have been welcoming physicists from around the world since 1951. Founded by French physicist Cécile DeWitt-Morette, the school has trained generations of early-career researchers, some of whom have since won Nobel prizes.
Application is open until December 8, 2025.
Further information:
The GTML 2025 workshop brought together a wide community of researchers for a full week of exchange at the intersection of geometry, topology, and machine learning.The GTML 2025 workshop brought together a wide community of researchers for a full week of exchange at the intersection of geometry, topology, and machine learning. With overwhelming interest, the event highlighted the growing momentum of this rapidly evolving research field.
The Workshop on Geometry, Topology, and Machine Learning (GTML 2025), jointly organized by the Max Planck Institute for Mathematics in the Sciences (Leipzig) and the STRUCTURES Cluster of Excellence (Heidelberg) took place recently in Leipzig. It marked the first event of this scale to unite the research communities of geometry, topology, and machine learning. The workshop attracted 132 participants, with registration reaching full capacity within only two weeks – a clear evidence of the strong interest within the scientific community.
GTML 2025 provided a unique platform for researchers to explore the fundamental role of geometric and topological methods in understanding data structures and developing rigorous frameworks for machine learning. The workshop format fostered deep scientific exchange and created valuable opportunities to identify new connections and build bridges between traditionally separate fields.
The scientific programme featured 10 keynote lectures and 20 expert presentations from leading researchers worldwide. A number of renowned speakers contributed to the programme, including industry experts Hartmut Maennel (DeepMind), Robert Lilow (Deepshore), and Vincent Stimper (Isomorphic Labs). Short papers will be published as a special edition of the PMLR (Proceedings of Machine Learning Research) series, ensuring continued visibility of the scientific contributions beyond the event itself.
A special highlight of the workshop were the Lightning Sessions, designed specifically for early-career researchers. These rapid-format presentations created a dynamic space for young scientists to share ideas, showcase ongoing work, and expand their professional networks.
The programme covered a broad spectrum of topics, including Mathematical foundations of machine learning, geometric machine learning (geometric deep learning, graph neural networks, geometry processing), topological machine learning (topological deep learning, TDA, shape analysis), and applications in the life sciences and complex systems.
Please visit the conference website for detailed information on the scientific topics.
With its strong scientific programme, interdisciplinary focus, and outstanding level of engagement, GTML 2025 has set a promising precedent for future meetings at the intersection of geometry, topology, and machine learning.
Further information:
- Conference Website GTML 2025
- Max Planck Institute for Mathematics in the Sciences
- Organizers: Michael Bleher, Freya Jensen, Levin Maier, Diaaeldin Taha, Anna Wienhard
We are delighted to announce the next event in our Machine Learning Galore! series, focusing on Scientific Machine Learning, which will take place on Thursday, November 13, from 4:30 to 6:00 pm at INF 205 Mathematikon (5th floor). The event features lab presentations by principal investigators, followed by brief presentations from junior scientists showcasing their latest work. Extended discussions will offer ample opportunity for in-depth exchanges.
Event Details:
- Lab presentations:
- Lukas Balles
- Jürgen Hesser
- Wolfgang Huber
- Science Talks:
- Constantin Ahlmann-Eltze (Huber lab): ML in Single Cell and Spatial Omics for Tissue Biology and Biomedical Research
- Pit Neitemeier (Balles lab): Learnt splitting and the influence of compression ratios in end-to-end hierarchical language modeling
- Marcus Buchwald (Hesser lab): Reaching for Causal Image Generation using deep conditioning
Registration is free but required via the ML-AI portal:
https://www.mlai.uni-heidelberg.de/en/machine-learning-talks-on-campus
About Scientific Machine Learning
Scientific Machine Learning is a collaborative initiative by the Interdisciplinary Center for Scientific Computing (IWR) and the STRUCTURES Cluster of Excellence. Its mission is to foster interaction and exchange within the local machine learning community, and to support its development by consolidating activities and resources that might otherwise remain scattered across individual institutions or disciplines. The initiative aligns closely with the objectives of STRUCTURES, which aims to advance fundamental research, and with IWR’s focus on applying machine learning to address long-standing challenges in the natural and life sciences, engineering, and the humanities.
Further information:
ENUMATH: International Conference Brings Together 600 Numerical Mathematicians in Heidelberg
From September 1 to 5, 2025, Heidelberg University welcomed the international numerical mathematics community to the Neuenheimer Feld campus for 2025's European Conference on Numerical Mathematics and Advanced Applications (ENUMATH). The event, supported by the STRUCTURES Cluster of Excellence, brought together more than 750 participants from over 30 countries and six continents, including leading experts as well as early-career researchers. Over the course of five days, mathematicians and computational scientists from all backgrounds shared their latest results, exchanged ideas, and explored new directions for applying numerical mathematics in science and industry.
Alongside invited plenary lectures by leading international experts, the scientific program consisted of over 600 talks in mini-symposia and contributed sessions, as well as a large poster session. Vibrant discussions spanned from advances in discretization schemes and multi-scale modelling to questions on uncertainty quantification, optimisation, and scientific machine learning – reflecting the broad scope and growing role of numerical mathematics in solving complex scientific and industrial challenges.
This year's conference was chaired by STRUCTURES member Robert Scheichl. The local organization committee included Peter Bastian, Roland Herzog, Vincent Heuveline, Guido Kanschat, Ekaterina Kostina, and Jakob Zech – members of STRUCTURES, IWR and IMa. The Programme Committee consisted of nine international experts from various countries across Europe. Their efforts, supported by numerous colleagues, institutions, and partners, made the conference a big success.
Since its launch in 1995, ENUMATH has established itself as a central forum for the exchange of ideas in numerical mathematics. The Heidelberg meeting continued this tradition by fostering international collaboration and providing a platform for early-career researchers to present their work alongside established experts. Proceedings of the conference will appear with Springer, extending the impact of the lively discussions in Heidelberg to the wider mathematical community.
For STRUCTURES, supporting ENUMATH was a way of underlining the importance of numerical mathematics for our mission to connect disciplines and methods across the natural sciences. It was a great pleasure to welcome the international numerical mathematics community to Heidelberg, a vibrant hub for mathematics and computational science in Europe.
Further information:
We are pleased to announce that our member Victor Ksoll will establish a new research group at the Institute of Theoretical Astrophysics (ITA), one of the participating institutes in STRUCTURES, starting in early 2026. Supported by funding from the Carl Zeiss Foundation, the group – titled “Machine Learning Solutions for Star Formation” (StarForML) – will develop innovative machine learning algorithms for the efficient analysis of astrophysical observational data.
The group’s research will focus in particular on star formation, a complex process spanning a vast range of sclaes from molecular clouds to individual protostars. Comparing theoretical predictions to observations requires solving so-called inverse problems, which are computationally intensive. Given the massive data volumes produced by modern telescopes, machine learning has become an indispensable tool for tackling this challenge in an automated fashion. Dr. Ksoll’s goal is to design machine learning methods for such inverse problems in astronomy to enhance our understanding of star formation while increasing the transparency and interpretability of these computational approaches. The group will also employ transfer learning techniques to bridge the gap between simulations and real observational data.
About Victor Ksoll
Victor Ksoll studied physics at the University of Heidelberg, where he also earned his doctorate in astronomy. His research included stays at the Institute of Planetology and Astrophysics in Grenoble, France, and the Space Telescope Science Institute in Baltimore, USA. Within STRUCTURES, he is involved in projects CP 1 (Cosmic Structure Formation) and CP 2 (From Dust to Planets) in addition to various Exploratory Projects. He is also a member of STRUCTURES' Young Researchers Convent (YRC).
Further information:
We are happy to announce the upcoming Heidelberg Physics Graduate Days at Heidelberg's Graduate School for Physics (HGSFP), supported by the STRUCTURES Cluster of Excellence. The courses take place from October 6 to 10, 2025, in the form of parallel block lectures in the mornings and afternoons.
The Heidelberg Physics Graduate Days, which take place biannually, serve as a dynamic platform for advanced students and researchers to broaden their perspective in physics by attending introductory courses on topics that are unfamiliar to them, or deepen their knowledge by attending specific courses that may be offered at a deeper level.
In addition to the block courses, the Department for Physics and Astronomy cordially invites interested participants and guests to the Hans Jensen Invited Lecture, which will be given by Mark Thomson on “Particle Physics: Today and Tomorrow” on Thursday, October 9, 2024, 17:30 at INF 308, HS1.
Further information:
Understanding complex quantum systems remains a central challenge in modern physics. These systems can display correlations, pairing, and exotic states of matter that are key to both fundamental science and future quantum technologies. Yet, many of these processes occur on spatial scales too small to be resolved even with advanced imaging techniques. In particular, while current single-atom imaging techniques are powerful, they fail once the relevant structures fall below the resolution limit of the detection method, leaving essential microscopic correlations hidden.
To overcome this fundamental limitation, Sandra Brandstetter and her colleagues from the group of STRUCTURES member Selim Jochim have developed a novel "matter-wave microscope". Before imaging the atoms, their approach first magnifies their wave function by a factor of about 50. This is achieved by precisely controlling the atoms' time evolution within specially designed optical potentials, essentially performing two “rotations” of the wave function in phase space, without disturbing the correlations that are of interest for their study.
This new technique unlocks the ability to access arbitrary higher-order correlations. Its applications extend to in-depth studies of fermionic pairing and other exotic systems; and help reveal the building blocks of future quantum technologies.
Further information: