May 21 – 24, 2024
Bernhäuser Forst
Europe/Berlin timezone

The Active Training Course "Advanced Deep Learning" will take place over 4 days at Bernhäuser Forst in Filderstedt. The event serves the professional education of young scientists belonging to the ErUM Community and is being organized by the community organization DIG-UM with support from the BMBF-funded ErUM-Data-Hub

The intensive course on Transformers, Normalizing Flows and Autoencoders will be held from 21.05.24 - 24.05.24. The course includes a challenge to be worked out and presented by participant subgroups. The workshop is aimed at deep-learning enthusiasts from all ErUM communities (Research on Universe and Matter) who have prior knowledge of neural networks and applied basic concepts of deep learning. 

A fee of 300€ will be charged for participation in the course. The workshop fee includes the cost of the workshop, accommodation and catering. Registration closes tba.

Read here about the first Active Training Course "Advanced Deep Learning" in November 2022.


Bernhäuser Forst
Dr.-Manfred-Müller-Straße 4 70794 Filderstadt


  • Autoencoder (Steffen Korn)
  • Transformers (Jonas Spinner)
  • Normalizing Flows (Thandikire Madula)
  • Model Diffusion (Paul Wollenhaupt)
  • Group Challenge



  • Christian Scheulen
  • Vladimir Starostin
  • Lukas Bauer
  • Arul Prakash



  • Prior knowledge of neural networks
  • You should also have programming expertise with Python and some knowledge of plotting with Matplotlib.


Important Info:

  • A fee of 300€ will be charged (includes the workshop, accommodation and full catering
  • Important information for participants of RWTH Aachen: RWTH members please contact your institute´s administration to pay the fee. The participation fee will be transferred for you via the institues.
  • At Bernhäuser Forst there is a possibility to do sports, hiking and swimming in your spare time. Remember to bring suitable clothing.


Tip for students:

  • Ask your institution if you can get ECTS credits for the school!


This workshop is supported by:

  • ErUM-Data-Hub (BMBF)