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recently developed mathematical theory, linking stochastic, network, and agent-based modelling, describing how this emergent behaviour supports essential maintenance and sharing of contents through
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lattice models for stochastic processes, including branching processes, and contribute to the supervision and mentorship of research students. The project sits within the broad area of Integrable
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and validate their methods using real-world systems. Postdoctoral fellows will work across the following research areas: Predictive machine learning Robust and stochastic optimization Learning-enabled
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and statistical mechanics. The main research areas include strongly correlated systems such as the Abelian sandpile; random interfaces such as the Gaussian free field; stochastic processes on graphs
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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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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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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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complexity theory. Language requirement: English requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations
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exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8 Grade requirements: The norm is as follows: the average grade point for courses included in
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/knowledge graphs, and carbon accounting. The Research Fellow will help develop and lead the CognitionX Lab (https://cognitionx-lab.github.io/ ) with Dr. Jinying Xu, Assistant Professor and Director of