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for self-supervised learning in science Engage in national and international ML/DL communities, most importantly the Helmholtz Foundation Model Initiative Present research results at scientific meeting
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the functional-structural plant model CPlantBox to include mycorrhization, growth of extraradical hyphae in soil, and water flow and nutrient transfer in the soil-plant-mycorrhiza system. Model parameterisation
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membranes. You will develop theoretical models and employ computational methods to describe the coupling between ionic nanofluidics, electric double-layer charging, and interfacial reactivity in electrolyte
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PhD Position - Modeling and simulation of memristive devices for application in neuromorphic systems
Your Job: The aim of this doctoral project is to develop physical simulation models for memristive devices. In particular, a compact model that describes the switching behavior of gradual switching
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hydrogen energy sector. Your Job: Conduct cutting-edge research to develop and validate advanced AI methodologies for the characterization, modeling, and simulation of energy materials. Develop code and
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on refining the ETHOS.MIDAS model developed in the last two years. You will identify and quantify new measures for decarbonization and adaptation to climate change. You will refine the existing model and expand
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communities. In particular, given the limited observability of the energy systems, advanced techniques are needed to model and estimate the behaviour of the entire energy system, in order to provide reliable
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Your Job: Synthesis and characterization of ceramic uranium oxide-based nuclear fuel materials and model systems comparable to commercial grades Application of microstructural and chemical analysis
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Your Job: You will be performing simulations of lattice quantum field theory (QCD/low dimensional models), developing analysis methods and tools and simulation software, analyzing, publishing and
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of Machine Learning on the Exascale computer JUPITER. In particular, your work will include: Developing, implementing and refining ML techniques suited for the largest scale Parallelizing model training and