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can be found at: https://www.kcl.ac.uk/mathematics About the role Applications are invited for a Lectureship in Applied Mathematics with a focus on quantitative modelling in the Department
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partnerships; Interface between academics in the Mathematics at the Interface and Fundamental Mathematics research groups and other partners in academia and industry; Learn about and implement new modelling
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Development of FEM 2.0 models based on CUF for the analysis of multiphysics problems - Ref. 21/26/CR
? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Development of FEM 2.0 models based on CUF for the analysis of multiphysics
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can be found at: https://www.kcl.ac.uk/mathematics About the role Applications are invited for a Lectureship in Applied Mathematics with a focus on quantitative modelling in the Department
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interactions between inclusions and the carrier fluid), as well as skills in statistics and stochastic modeling (applied mathematics or statistical physics). The candidate must be at the interface between
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understanding of algebraic topics. Help students connect real-world PBL contexts to formal mathematical principles. Assist peers in interpreting, modeling, and solving complex problems within each PBL unit. 3
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21 Apr 2026 Job Information Organisation/Company CITMAga - Galician Center for Mathematical Research and Technology Department R&D&i Unit Research Field Mathematics » Statistics Mathematics
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for the MODFLOW 6 Groundwater Transport Model, Techniques and Methods. Reston, VA Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR6074-ARNBLO-001/Default.aspx Requirements Research FieldComputer
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students. Research areas within mathematical statistics are probability theory and statistical theory. The main task of probability theory is to develop mathematical models for the description and analysis
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network dynamics, including (i) modeling questions in mathematical neuroscience, with the aim of motivating and testing the theoretical framework, and (ii) inference of network structure from time-series