PostDoc - Integrated Subsurface Hydrology-vegetation Modelling
Max Planck Institute for Biogeochemistry
Jena, Germany
Background and position description
The "Machine Learning for Hydrological and Earth Systems" group within the Department of Biogeochemical Integration at the Max Planck Institute for Biogeochemistry is looking for a highly motivated postdoctoral researcher. This position is part of the CZS-funded project “Knowledge Integration for Spatio-temporal Environmental Modelling.” The research will focus on an integrated modelling framework that combines process-based hydrological models (e.g., considering hillslope topography and subsurface flow), ecosystem dynamics models, and hybrid machine learning tools. The ultimate goal of the project is to improve our understanding of how topography and subsurface hydrological processes modulate the impacts of climate change on vegetation dynamics and the water cycle. A key aspect of this work will be to consider the hybrid modelling approach to assimilate different data sources (e.g., satellite-derived vegetation structure, soil moisture, and flux observations) to validate, calibrate, and refine the integrated modelling system. The project aims to address questions such as what are the key processes governing water storage and vegetation water sourcing along topographic gradients.
Your tasks
- Develop and apply integrated modelling frameworks to study subsurface hydrology, plant water use, and ecosystem responses
- Analyse and assimilate spatially resolved datasets (e.g., satellite observations of vegetation structure and soil moisture) to improve model accuracy and predictive capabilities
- Investigate how topography and subsurface hydrological processes mediate climate change impacts on vegetation dynamics and the water cycle
- Disseminate research findings through peer-reviewed publications and presentations at international conferences
- Assist in the supervision of doctoral researchers, contributing to their academic and professional development
Your profile
- A PhD in ecohydrology, hillslope hydrology, environmental science, Earth system science, biogeosciences, climate science, or a related field
- Expertise in hydrological and ecological modeling approaches or land surface models
- Experience in hybrid and explainable machine learning methods is an advantage
- Proficiency in programming and analysis tools such as Python, R, or MATLAB
- Experience in publishing scientific papers, preferably on topics such as ecohydrology, water-carbon coupling, hillslope hydrology, or AI-powered environmental analytics
- Strong teamwork and collaboration skills, with a responsible and proactive attitude
- Ability to work both independently and as part of a team
- Excellent communication skills (oral and written) in English
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