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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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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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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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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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ReGroStaFT, which develops rigorous mathematical approaches to renormalization and coarse-graining for models from statistical physics, particle systems, and stochastic partial differential equations
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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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deployment enabling validation and demonstration of real-world applications. For more details, please view https://www.ntu.edu.sg/erian Multi-Energy System & Grids Team is looking for a Research Fellow 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
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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