We are inviting applications for a
PhD position (m/f/d; E13 TV-L, 65%)
to develop seasonal forecasts of extreme rainfall events over Germany and western Europe using spatiotemporal artificial neural networks (STANNs). The position is funded for three years starting no later than 1 September 2021 and, along with three other PhD projects, is part of the mini-graduate school Modeling and Understanding SpatioTemporal Environmental INteractions (MUSTEIN).
MUSTEIN aims at developing machine learning (ML) techniques that reliably learn explainable models of critical aspects of four highly interacting spheres, focusing on (i) seasonal weather dynamics (P1), (ii) river water discharge (P2), (iii) soil erosion (P3), and (iv) solar thermal systems (P4). The targeted ML-based systems will potentially allow us to (i) predict environmental system dynamics more accurately and for longer periods into the future, (ii) anticipate future climatic developments and prepare accordingly, (iii) partially control the developments, and (iv) explain the hidden causes and influences in an accessible, causal manner.
To apply for the position, please follow the instructions given in the full job announcement here or read it online at this link.
For any further queries please send an email to Dr. Bedartha Goswami at firstname.lastname@example.org.