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This PhD project will investigate partial differential equations arising from kinetic models, with a particular focus on variational methods. The research will explore how variational structures
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of analysis of partial differential equations (PDEs). The PhD candidate will be supervised by Havva Yoldaş and co-supervised by Raphael Winter (Cardiff University). The research topics primarily revolves around
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Microlocal analysis explores the classical/quantum correspondence in partial differential equations (PDEs). For example, the concept of symbol quantisation in the theory of pseudodifferential
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differential equations, -- excellent oral and written communication skills. Prior experience in computational fluid dynamics or active matter will be a big advantage, but we seek, above all, a willingness to
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Parametric integrators approximate solutions of (high-dimensional) partial differential equations by restricting the numerical approximation to a closed form that nonlinearly depends on, and is
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of an object or medium from measurements of waves that have propagated through it. Mathematically, such problems lead to large-scale nonlinear inverse problems governed by partial differential equations (PDEs
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imaging systems. These methods naturally lead to challenging mathematical models, including nonlinear partial or ordinary differential equations. However, real-world optical systems are subject to
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new approach has emerged that integrates data and mathematical models through neural networks. This has led to the development of a method for solving partial differential equations (PDEs) known as
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; a strong background in one or more of the following areas: partial differential equations, inverse problems, numerical methods for PDEs, scientific computing; the ability and interest to work with
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and Dynamical Systems group and in Lund it is embedded in the Partial Differential Equations group . You will be able to participate in a range of local and national seminars. There is also funding