Position PhD project "Hybrid modeling of root zone water storage and ecosystem responses to water availability" Employer Max Planck Institute for Biogeochemistry The Max Planck Institute for Biogeochemistry (MPI‑BGC) houses a unique and flexible research program that grants German and foreign students broad learning opportunities while maintaining a research focus. The International Max Planck Research School for Global Biogeochemical Cycles (IMPRS‑gBGC) offers a PhD program specializing in global biogeochemistry and related Earth system sciences. Homepage: Location Jena, Germany Sector Academic Relevant divisions Biogeosciences (BG) Hydrological Sciences (HS) Soil System Sciences (SSS) Type Contract Level Entry level Salary Open Preferred Education Master Application deadline 6 August 2026 Posted 30 June 2026 Job Description Project description Floods are among the most damaging environmental hazards, yet predicting them where they matter most remains difficult. Most of the world's catchments are ungauged, and the records we do have are often short. Climate change and land surface alterations make the past an increasingly unreliable guide to the future. Purely data‑driven models have made remarkable progress by learning across many catchments at once, but they remain constrained to the range of conditions they have seen, and the cases we care about most – unprecedented extremes and a shifting climate – lie outside that range by construction. This project will advance hybrid, physics‑aware machine learning for flood prediction that aims to generalize beyond observed conditions. Building on ADELM ( our differentiable ecohydrological land surface model developed openly in the group, the project will develop a model chain that runs from land‑surface processes through the river network to flood inundation, trained end to end against observations. A key focus will be generalization across space and time, including ungauged regions, non‑stationary climatic and land‑surface conditions, and rare extreme events. The successful candidate will work at the interface of hydrology, land surface modeling, and machine learning. The project offers the opportunity to develop hybrid and differentiable modeling techniques, to combine process understanding with large observational datasets, and to improve the reliability with which we can anticipate floods under environmental changes. Working group
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EGU Solar-Terrestrial Sciences Division Recruiting-Team
