PhD Call for PINM: Process-based integrated modeling of rhizosphere N cycling for improved plant N uptake 

PINM: Process-based integrated modeling of rhizosphere N cycling for improved plant N uptake

Upscaling N cycling from the rhizosphere to the plant by coupled process-based modeling with machine learning for understanding how soil-plant-microbiome interaction affects N-use efficiency in sustainable agro-ecosystems

Plant–soil–microbe systems are governed by physical and biological processes that can be described by mathematical equations such as partial and ordinary differential equations (PDEs and ODEs). While classical process-based models capture many of these dynamics, they require parameterization and boundary conditions. Recent advances in scientific machine learning (SciML) offer a new way forward. Physics-informed and biologically informed neural networks (PINNs and BINNs) integrate knowledge of physical laws with experimental data. We aim to develop such hybrid models.

Microbial N cycling in the rhizosphere is critical to the N-use-efficiency of plants. Understanding how these microbial processes interact with plant roots is key to improving nitrogen use efficiency and developing more sustainable cropping systems. In this project, you will explore how to couple microbial processes with whole-plant growth models using hybrid modeling.

What makes this project exciting?

• You will contribute to advance digital twinning in Bio- and Geosciences by integrating mathematical modeling and machine learning.
• Your project is embedded in a highly collaborative research environment, including cross-institute synergies.
• You will have opportunities to collaborate with other Simulation and Data Lab (SDL) initiatives, such as Terrestrial Systems (IBG-3/JSC), which aims to upscale soil–plant models from root and field to regional and Earth system scales.
• You will benefit from strong collaborations with large research initiatives, including the BMFTR-funded BonaRes-Rhizo4Bio initiative (e.g., CROP project) and the DFG Cluster of Excellence PhenoRob (e.g., the PhD project HySoMi on hybrid microbiome modeling).