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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 equation models of bacterial persistence. A particular challenge, both for simulation and for machine learning, lies in the high dimensionality of these equations, which causes grid-based numerical
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Mathematics at the University of Twente. You will work under the supervision of Dr. Gregor Gantner on a project dedicated to advancing the field of adaptive methods for time-dependent partial differential
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condensed matter physics • Ability to learn and develop skills in analytical computation, theoretical modelling and numerical simulations, in particular the numerical solution of partial differential
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mathematical modelling and simulation. Partial differential equations will be applied to model drug distribution and partitioning in multiphase systems using the drug’s partition coefficient as a driving force
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and solution of nonlinear partial differential equations (PDEs); Experience with at least one scientific programming language (Python, C++, or Fortran); Ability to work independently while integrating
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combined approach using numerical modeling and environmental metrology Key words: simulation, modeling, partial differential equations, hydraulics, inverse problem, sediment transport, peri-urban catchment
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differential equations on existing published datasets. In the latter stages, this project will also involve the generation of new data in emerging applications. As a PhD student, you will address the forecasting
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Mathematics, Physics, or a closely related field. Strong background in partial differential equations and stochastic analysis and a genuine interest in its applications to fluid dynamics or related areas
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, differential equations, integral calculus, probability theory, numerics) Proficiency in programming with Python (in-depth understanding of programming concepts, native Python and efficient use of libraries