Cluster of Excellence “Machine Learning”
Maria-von-Linden-Str. 6
72076 Tübingen
+49 7071 2970896
felix dot. strnad at uni-tuebingen dot. de

Research Interests

I am a complex systems physicists with a special interest on:

  • Nonlinear time series analysis tools like climate networks and probilistic machine learning methods
  • Global teleconnection structures of modes of variability (e.g. El Nino-Southern Oscillation)
  • Spatial Organization of Extreme Rainfalls

A more detailed project description can be found here.


I obtained my Bachelor in Physics at the Georg-August University of Göttingen, followed by a specialization in Master of Physics of Complex Systems at the University of Göttingen. During my universtiy studies I spent one year at the University of Pisa. My Master’s thesis I wrote at the Potsdam Institute for Climate Impact Research (PIK). I spent one further year as a research assistant at PIK in the Real Estimate Climate Asset Mapping Project (RECAM). Since September 2020, I’m a PhD student in the Machine Learning in Climate Science group at the University of Tübingen and I’m part of the International Max-Planck Research School for Intelligent Systems (IMPRS-IS). Beside science, in my free-time I play in an orchestra, love to go running and doing sports.

My CV can be found here.


2019 2022


  • Felix M. Strnad, Jakob Schlör, Christian Fröhlich, and Bedartha Goswami, Teleconnection patterns of different El Nino types revealed by climate network curvature, Geophysical Research Letters, (2022), doi.org/10.1029/2022GL098571

  • Philipp Hess, Markus Drüke, Stefan Petri, Felix M. Strnad, Niklas Boers; Physically Constrained Generative Adversarial Networks for Improving Precipitation Fields from Earth System Models; Nature Machine Intelligence, (2022), doi.org/10.1038/s42256-022-00540-1


  • Felix M. Strnad, Wolfram Barfuss, Jonathan F. Donges and Jobst Heitzig, Deep reinforcement learning in World-Earth system models to discover sustainable management strategies, Chaos (2019) doi.org/10.1063/1.5124673