Introducing ML to scientists
Working together on applications
We advise and collaborate with domain researchers. Here are some of our latest projects
- PollenClim: Working with archaeologists and paleoclimatologists, we improve our understanding of the past climate from a combination of simulations and fossilized pollen data
- CoBaRd: We are currently working with physicists to design a device (moderator) to reduce cosmogenic background by finding the parameter set that is predicted to maximally reduce the number of neutrons as predicted by Monte Carlo simulations in GEANT.
- LiTrace: We are currently working physicists to obtain position (xyz), time and energy, parameters of scintillation from the currents generated at photodetectors (NNVT MCP-PMTs) placed on the wall of the vessel.
Techniques: Gaussian Processes, AutoML
Reproducible machine learning
The Machine Learning ⇌ Science Colaboratory is part of the Cluster of Excellence Machine Learning: New Perspectives in Science at the University of Tübingen. We are a passionate team of researchers and engineers working to increase the impact of machine learning (ML) on the sciences and the humanities.