Machine Learning in Climate Science

In January 2020, Dr. Bedartha Goswami  joined the Cluster of Excellence 'Machine Learning' at the University of Tübingen as head of the independent Junior Research Group, "Machine Learning in Climate Science." The group will extend and develop machine learning based concepts to explore, quantify, and understand complex climatic phenomena such as the El Niño Southern Oscillation and the global monsoon.

Bedartha has worked at the Potsdam Institute for Climate Impact Research (PIK) with Dr. Norbert Marwan on a DFG funded project (2017-2020) that focuses on the development of a new framework to work with uncertainties in climate data. Prior to that, he has worked with Prof. Dr. Bodo Bookhagen at the Institute of Geosciences, University of Potsdam, on characterising spatial patterns of hydrology in the greater Himalayas and also on estimating flow accumulation from lidar point clouds (2016-2017). Bedartha did his PhD (2015) under the supervision of Prof. Dr. Dr. h.c. Jürgen Kurths at PIK, on developing new ways to deal with uncertainties in sedimentary proxy records obtained from paleoclimate archives such as stalagmites, lake sediments, and ice cores.

His research interests include nonlinear time series analysis and recurrence plots, complex networks based analysis, uncertainties in data science methods, and in particular, the role of data uncertainties in shaping our understanding of complex real-world phenomena such as synoptic-scale climatic systems.

  I am currently looking for PhD students (fully funded by the Cluster).
     Please send me an email in case you are interested.



Dr. Bedartha Goswami
Machine Learning in Climate Science

University Tübingen
Cluster of Excellence "Machine Learning"
Maria-von-Linden-Str. 6, 4th floor
Room No. 40-5/A10
72076 Tübingen

+49 7071 2970894