PhD project offered by the IMPRS-gBGC in July 2023

Deep learning of ecosystem functioning and carbon fluxes

Christian Reimers , Martin Jung , Sophia Walther , Markus Reichstein , Alexander Brenning

Project description

Data-driven approaches to mapping ecosystem carbon fluxes globally are promising tools for monitoring and advancing our understanding of the global carbon cycle. This approach uses machine learning of in-situ measured carbon fluxes from eddy-covariance towers based on co-located meteorological and remote sensing data. However, the approach is inherently limited by the spatially sparse and clumped distribution of only a few hundred flux towers which hampers learning complex relationships of how ecosystems respond differently to weather and climate.
This PhD project aims at improving machine learning based representations of ecosystem functioning and carbon flux variations by combining the sparse in-situ measurements with densely observed remote sensing data such as sun induced fluorescence in the learning process. Methodologically, this relates to machine learning concepts like transfer learning, multi-task learning or domain adaptation, and challenges include mapping the implicit functional encoding explicitly in space.

Working group & planned collaborations

The successful candidate will work in the Department of Biogeochemical Integration at the Max-Planck- Institute for Biogeochemistry. The working group provides long-standing expertise and experience of the different fields relevant to that project: Machine learning (Christian Reimers, Alexander Brenning, Markus Reichstein, Martin Jung), Remote sensing (Sophia Walther, Martin Jung), flux tower measurements (Markus Reichstein, Martin Jung), carbon cycle (Markus Reichstein, Martin Jung, Sophia Walther) and ecosystem functioning (Markus Reichstein, Martin Jung, Sophia Walther). The candidate will benefit from and closely collaborate with the FLUXCOM team at MPI-BGC on the overall methodological approach. Further collaborations with members of the Michael-Stifel-Center Jena for Data-driven and simulation science as well as within the European Lab for Learning and Intelligent Systems (ELLIS) are likely to emerge.

Requirements for the PhD project are

Applications are open to highly motivated and independent students from any country who have:

A Master's degree in Computer Science, Physics, Environmental Sciences, Geomatics or a related discipline
programming experience, preferably in Python
background in machine learning, preferably neural networks and deep learning
interest in global ecosystem science
Very good oral and written communication skills in English

The Max Planck Society (MPS) strives for gender equality and diversity. The MPS aims to increase the proportion of women in areas where they are underrepresented. Women are therefore explicitly encouraged to apply. We welcome applications from all fields. The Max Planck Society has set itself the goal of employing more severely disabled people. Applications from severely disabled persons are expressly encouraged.