Imperial College London


Faculty of Natural SciencesThe Grantham Institute for Climate Change

Research Associate (James S McDonnell Foundation Fellowship)







Sherfield BuildingSouth Kensington Campus





I'll soon be moving to become a group leader in the new DLR Data Science Institute in Jena, Germany. If you're interested in doing a Postdoc / PhD applying machine learning to climate research and beyond, visit here and contact me! 

Jakob is a complex systems scientist with a focus on causal discovery techniques from high-dimensional, nonlinear time series. His main research interests are causal inference algorithms using advanced machine learning techniques, causal complex network theory, information flow in complex systems, and nonlinear prediction. Jakob collaborates with researchers from many applied fields to help in better understanding real world complex systems, in particular the climate system.

Jakob studied physics at Humboldt University Berlin funded by the German National Foundation (Studienstiftung). In 2014 he obtained his PhD on causal inference from dynamical complex systems at the Potsdam Institute for Climate Impact Research and Humboldt University Berlin, again funded by the German National Foundation. For his thesis he was awarded the Carl-Ramsauer doctoral thesis prize by the Berlin Physical Society. In 2014 he won a Postdoctoral Fellowship Award in Studying Complex Systems by the James S. McDonnell Foundation and is now a Research Associate at the Grantham Institute at Imperial College.

Jakob's research was published in Nature Communications, Physical Review Letters, and Journal of Climate, among others. On he provides Tigramite, a time series analysis python module for causal inference.



Kretschmer M, Coumou D, Donges JF, et al., 2016, Using causal effect networks to analyze different Arctic drivers of mid-latitude winter circulation, Journal of Climate, Vol:29, ISSN:0894-8755, Pages:4069-4081

Donges JF, Heitzig J, Beronov B, et al., 2015, Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package, Chaos, Vol:25, ISSN:1054-1500, Pages:113101-113101

Runge J, Donner RV, Kurths J, et al., 2015, Optimal model-free prediction from multivariate time series, Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol:91, ISSN:1539-3755


Runge JGB, 2016, Tigramite, v.2.0 beta

Donges JF, 2015, Pyunicorn

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