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of spikes by a model Develop proxy apps representing the different processing stages of spiking network simulation code (targeting CPU and accelerators such as GPU or IPU) Systematic benchmarking of proxy
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these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High resolution analysis, monitoring
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modeling with experimental validation and has two major objectives: Development of a physics-informed neural network (PINN) framework You will design and implement a simulation framework to model
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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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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 describe their impacts on biodiversity and ecosystem services. To do this we use a combination of diverse methods, from empirical research to remote sensing and simulation modeling. A quantitative
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synaptic resilience and the reliability of synaptic responses. The work primarily involves mathematical modeling and numerical simulation, but also the analysis of experimental datasets for model validation
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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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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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position in the area of machine learning and computer simulations. The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement