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well as LiDAR measurements, into ensemble agroecosystem model simulations. The successful candidate will play a key role in developing robust landscape-scale digital twins and advancing data assimilation
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and simulation modeling. A quantitative understanding of ecosystem dynamics provides the foundation for the development of robust management concepts for the sustainable provisioning of diverse
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the implications for forest ecosystem service supply. The main methodological approach applied will be forest landscape simulation modelling using the model iLand. The work is embedded in the BETA-FOR project (https
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approach (CPlantBox) to mechanistically simulate plant growth, plant-soil interactions and the rhizosphere microbiome. You will contribute to model development and apply it to disentangle the role of root
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and extend existing numerical codes to simulate these phenomena. Some experiments and modelling will be done in collaboration with other PhD students in the GRAIL project. Your tasks: • Simulate
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avionics triggered by the interaction of the energetic particles with the electronics. The project will adapt and extend existing numerical codes to simulate these phenomena. Experiments and modelling will
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learning and computer simulations. The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement tasks to work with, enhance
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NEST: https://nest-simulator.readthedocs.io Your tasks in detail: Work with the NEST main code base and experimental branches Dissect the spiking network simulation cycle into phases and capture the flow
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Your Job: This PhD project focuses on modelling and simulating future gas grids, exploring transformation pathways, and developing cross-sectoral simulation frameworks to support informed decision
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Approach ( https://vhrz669.hrz.uni-marburg.de/ssf/ ). Understanding the mechanisms controlling subsurface flow (SSF) and the conditions under which it occurs remains a major challenge in hydrology and