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(PDE). Examples of models in the scope of the project include particle models, stochastic PDE and models from fluid dynamics and machine learning. Place of work is the Department of Mathematics, Blindern
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chemical composition of biological nanoparticles, notably extracellular vesicles (EVs) and microbacteria. Current methods to characterize these particles are too slow and bulky. We want to make an on-chip
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particle models, stochastic PDE and models from fluid dynamics and machine learning. What skills are important in this role? Qualification requirements: The Faculty of Mathematics and Natural Sciences has a
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of partial differential equations (PDE). Examples of models in the scope of the project include particle models, stochastic PDE and models from fluid dynamics and machine learning. What skills are important in
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fast charging is the surface intercalation kinetics or the solid-state diffusion of lithium within the particles. Finally, the insights gained will be applied to optimize material properties, such as
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the particles. Finally, the insights gained will be applied to optimize material properties, such as crystallinity, particle size, and surface coatings, in collaboration with industrial partners who can provide
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within the particles. Finally, the insights gained will be applied to optimize material properties, such as crystallinity, particle size, and surface coatings, in collaboration with industrial partners who
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(EVs) and microbacteria. Current methods to characterize these particles are too slow and bulky. We want to make an on-chip system to speed up the process and make it available to do research
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(EVs) and microbacteria. Current methods to characterize these particles are too slow and bulky. We want to make an on-chip system to speed up the process and make it available to do research