International Conference on Machine Learning in Natural Sciences in Hamburg

19 September 2022

Photo: Christof Weitenberg, UHH

Machine learning is emerging as a vital tool in the natural sciences enabling new breakthroughs, e.g. in protein folding, material discovery, or efficient representation of quantum states. From September 19th to 22nd 2022 international researchers from different fields gathered in Hamburg for the conference "Machine Learning in Natural Sciences: From Quantum Physics to Nanoscience and Structural Biology" to discuss the latest developments and identify new connections.

The conference had 14 invited speakers and 35 external participants and 32 participants from Hamburg University, contributing 20 talks as well as posters. The participants came from 10 countries in Europe, but also from Canada and Japan. The great atmosphere in the CFEL building gave rise to fantastic informal discussions as well as a panel discussion. The conference was organized together with the ICFO and financed by the Excellence Cluster CUI.

Check out the conference website for more information and the lecture2go site for video recordings of the talks.

Latest articles

Plot of the sensitivity of the proposed experiment to the coupling constant of scalar field dark matter to visible matter as a function of the mass of the conjectured dark matter particle

Photo: adapted from PRD 107, 015008 (2023)

18.10.2022|AG Weitenberg

Searching for scalar field dark matter with hyperfine transitions in alkali atoms

The nature of dark matter is one of the largest unsolved problems in modern physics and precision measurements with cold atoms might be able to detect candidate dark matter particles. Ultralight scalar field dark matter is expected to cause oscillations in fundamental constants that could be...

Sketch of the multi-frequency-lattice scheme: the laser beams with different frequency components for three one-dimensional lattices

Photo: UHH

11.07.2022|AG Weitenberg

Multi-frequency optical lattice for dynamic lattice-geometry control

We have developed a new scheme for tunable optical lattices that can switch, ramp or modulate between a honeycomb, boron-nitride and triangular lattice. We use it to prepare higher bands and to introduce a sublattice-modulation spectroscopy. Furthermore, we introduce the geometry phase as a new...