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supporting air quality research, policy development and evaluation of air pollution interventions. This role uses numerical models of atmospheric chemistry and transport (GEOS-Chem, and others) to increase
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provision of user-tailored climate services including products related to climate modeling. The successful candidate will have experience in two or more of the following areas: numerical climate modeling
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engineering, computer science applied to urban infrastructure, data science, or a related field. Expertise in numerical modeling and simulation for urban systems. Proficiency in digital twin tools and platforms
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an appropriate autogenous mill for pilot testing. Lead the design and execution of lab-scale autogenous grinding trials based on experimental design. Contribute to the numerical modeling of beneficiation circuits
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between objects. A common way to represent a graph is to use the adjacency matrix associated with the graph. However, adjacency matrices only model networks with one kind of objects or relations between
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research and innovation at the heart of its educational project as a driving force of a business model. In its research approach, the UM6P promotes transdisciplinary thinking, an entrepreneurial spirit, and
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trials based on experimental design. Contribute to the numerical modeling of beneficiation circuits (e.g., using USIMPAC or similar software). Analyze, interpret, and report the outcomes of simulations and
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. Expertise in numerical modeling and simulation for urban systems. Proficiency in digital twin tools and platforms such as AnyLogic, Unity, MATLAB/Simulink, or AWS IoT TwinMaker. Experience in integrating real
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that has been investigated. Fertilization experiments have confirmed that K deficiency limits tree growth and forest productivity and mechanistic models have been developed that confirm the strong response
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way to represent a graph is to use the adjacency matrix associated with the graph. However, adjacency matrices only model networks with one kind of objects or relations between the objects. Many real