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Field
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Partial Differential Equations, Dynamical Systems, Optimization, Scientific Computing, Complex Systems, and Network Science. Spanish language skills are not required. Our working language is English. Both
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contribute to undergraduate and graduate teaching in areas such as Numerical Methods, Ordinary and Partial Differential Equations, Dynamical Systems, Optimization, Scientific Computing, Complex Systems, and
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of Fourier series, non-periodic functions, application of Fourier series in the solution of partial differential equations. Dynamical systems: systems of linear differential equations, coupled systems
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for partial differential equations, and multiscale partial differential equations. The candidate will develop efficient numerical methods for solving the problems, and implement the numerical algorithms
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algorithms and neural network models, drawing on probability, partial differential equations, large deviations, stochastic analysis, optimisation and machine learning. The successful candidate will contribute
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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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-month, full-time (1.0 FTE), position. The search is open to all areas of mathematics, with candidates in partial differential equations (PDE) and algebra especially encouraged to apply. Candidates should
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also maintains strong research activity in partial and ordinary differential equations, dynamical systems, and systems and control theory, alongside active work in mathematical physics, including quantum
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of computational fluid dynamics (CFD). Mastery of applied mathematics and numerical methods. Resolution and discretization of partial differential equations (PDEs) and the algorithm applied to the simulation
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on probability, partial differential equations, large deviations, stochastic analysis, optimisation and machine learning. The successful candidate will contribute to the activities of the Machine Learning & Data