PostDoc Researcher in the field of plant modelling at Jülich, Germany
100% (E13 TVöD) | fixed-term, initially until December 31st, 2025, with the possibility of an extension
In project C02, we are looking for a Postdoc researcher in the field of plant modelling, with a background in plant physiology, drought stress and, ideally, experience in functional structural plant modelling and/or continental scale modeling (CLM5).
PhD Position - Terrestrial Modeling and Data Analytics PhD Position, Jülich, Germany
At the Institute of Bio- and Geosciences – Agrosphere (IBG-3) we conduct research to improve our understanding of biogeochemical and hydrological processes in terrestrial systems. Specific studies focus on environmental controls on biogeochemical cycling of elements, the analysis of exchange processes and nutrient dynamics in the soil-plant-atmosphere continuum. A combination of experiments, modelling and innovative observation technologies is used to bridge the gap between model, process and management scale. Our research contributes to the sustainable and resource-conserving use of soils and water and to the quantification of the effect of climate and land use change on terrestrial ecosystems. We offer a competent and interdisciplinary working environment, as well as an excellent framework in the areas of experiments and modelling.
Your Job:
- Develop and apply coupled terrestrial models in the research areas hydrogeology (groundwater), hydrology (soil and land surface including vegetation), and meteorology (weather/climate)
- Perform scientific simulations in supercomputer environments
- Analyze large geoscientific data sets
- Present and publish results at international conference and peer reviewer journals
Further positions:
PhD position: Scientific Discovery in Earth System Science: Applying Deep Learning Technologies in the Exploration of Big Data from Terrestrial Modeling
You will mine and explore big data from Terrestrial Modeling of the coupled water, energy, and nutrient cycle from groundwater to the top of the atmosphere. These type of simulations result in very large data sets (O(1000) TB) with hundreds of output variables that are correlated over short and long distances and timer periods via complex non-linear processes. Identification and characterization of these correlations and previously unidentified interacting mechanism constitutes a grand challenge in the Earth Science; only a fraction of available information from these simulations can be used today to predict, for example, extreme events such as drought and floods. In this project we tackle this challenge by developing and applying state of the art machine and deep learning technologies in massively parallel supercomputer environments leading to novel data mining and exploration capacities and new discoveries of previously unknown information in big simulation data. The project will be conducted in collaboration with the German Aerospace Center (DLR), and the Centre of High-Performance Computing in Terrestrial System, Geoverbund ABC/J.
Further positions:
