3 million euros for models in astro- and particle physics
A major unsolved problem in fundamental physics is that even the best theories -- the Standard Models of Cosmology and Particle Physics -- only explain a fraction of the Universe: 95% of the energy in the Universe appears to be composed of “dark matter” and “dark energy”. What are they exactly? Currently, physicists can only speculate what the laws of nature look like that explain these phenomena.
To answer these questions experimentally, researchers in astro- and particle physics face the challenge of analyzing exabytes of data that modern particle colliders, such as the Large Hadron Collider at CERN or telescopes like the Square Kilometer Array, produce - a perfect proving ground for AI. Two professors working together at the ORIGINS Cluster of Excellence and the Munich Center for Machine Learning, Prof. Lukas Heinrich (TUM School of Natural Sciences) and colleague Prof. Daniel Grün (LMU Physics) are primary investigators in the project from the Munich region.
The Foundation Models in Astro- and Particle Physics (SciFM) project, funded by the German Federal Ministry of Research, Technology and Space (BMFTR), aims to create a new generation of advanced AI tools, so-called foundation models, to enable physicists to extract the best possible information out of the gigantic scientific datasets available to them. Instead of training small “AI specialists” to solve individual tasks, foundation models are “AI generalists” that are trained on data from a wide range of sources and capable of solving many tasks. Because they can see data from multiple experiments at once, they can outperform what a model trained on data from any single experiment could achieve.
The project brings together a group of leading researchers in AI for fundamental physics across the country: Munich, Heidelberg, Hamburg, and Aachen, and has been funded with over 3 million euros. The project is now hiring postdoctoral researchers and PhD students.
More information and links
- Munich Center for Machine Learning: https://mcml.ai/
- ORIGINS Cluster of Excellence: https://www.origins-cluster.de/
- German Federal Ministry of Research, Technology and Space: https://www.bmftr.bund.de/ (in German)
- INSPIRE Jobs Board: https://inspirehep.net/jobs/3084302
Contact
Prof. Lukas Heinrich
Assistant Professorship of Data Science in Physics
TUM School of Natural Sciences
l.heinrich(at)tum.de
https://tupheds.github.io/tupheds_website/
Prof. Daniel Grün
Professor for Astrophysics, Cosmology, and Artificial Intelligence
LMU Faculty of Physics
Daniel.Gruen(at)lmu.de
https://www.physik.lmu.de/observatory/en/research/cosmology/acai-group/
Press contact
communications(at)nat.tum.de
Team website