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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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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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is aimed at prospective PhD students who wish to conduct research on the fascinating topic of “dust in the Earth system” and who wish to combine model development with the investigation of the role
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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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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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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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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 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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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
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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