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Statistics is opening a PhD position in mathematics focusing on the analysis of Partial Differential Equations. The position covers four years of third-cycle studies, including participation in research and
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solid knowledge of and show interest in mathematical analysis, partial differential equations and related subjects and is expected to have completed related courses. Documented knowledge in kinetic theory
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data analysis, while contributing to fundamental questions in evolutionary biology. We welcome applicants with training in biology or related quantitative fields (mathematics/physics/computer science
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in statistical analysis, quantitative methods, or mathematical modelling obtained outside these subject areas may also be included. The requirements do not need to be fulfilled at the time of
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a focus on topological data analysis, to a wide range of application driven questions. A primary interest is developing reliable methods based on rigid mathematical foundations targeting visualization
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at least 30 credits must be at the advanced (master's) level. Courses in statistical analysis, quantitative methods, or mathematical modelling obtained outside these subject areas may also be included
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aluminium through AI-driven microstructural analysis. About us The PhD candidate will work at the Division of Data Science and AI , in the neuro-symbolic research group. This group works with combinations
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applicant must have passed courses within the first and second cycles of at least 90 credits in either, a) Chemistry/Molecular Biology/Biotechnology, or b) Computer Science/Mathematics/Physics and at
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We are looking for a highly motivated, skilled, and persistent PhD student with experience in computational fluid dynamics (CFD) and some knowledge in structural analysis. The research aims
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. The research group led by Martin Enge is specialized in methodology-driven analysis of patient data, especially in the field of single-cell multiomics. We are a multidisciplinary group with expertise in both dry