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of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation, numerical methods
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Deep Learning algorithms for the numerical solution of Partial or Stochastic Differential Equations (PDES or SDEs) that arise in finance and economics. Specifically, highly complex problems involving
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physical-mathematical models expressed in terms of differential equations will be considered. In particular, the candidate will develop methods at the interface between statistics and numerical analysis, and
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with strong potential to complement and strengthen current departmental expertise in areas such as Differential Equations, Computational Mathematics, Convex Geometry, Statistics, and Mathematics
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Matrix Theory, Stochastic Differential Equations and Stochastic Processes, Partial Differential Equations and Industrial Mathematics, Topology and Dynamical Systems, Statistics and Applied Statistics
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problems in partial differential equations inspired by cutting-edge algorithms in statistics and machine learning Type of programme/project/ undertaking MAESTRO-16 Funding institution National Science Centre
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differential equations (PDEs). While all applicants with a background in the analysis of PDEs will be considered, candidates with prior experience in theoretical physics, fluid mechanics, kinetic theory
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strong research promise in at least one of the following research domains: ◦ geometric analysis; ◦ spectral theory; ◦ partial differential equations. Previous experience in the area of the project is an
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, Stochastic Differential Equations and Stochastic Processes, Partial Differential Equations and Industrial Mathematics, Topology and Dynamical Systems, Statistics and Applied Statistics, Numerical Analysis
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interacting primarily through the strong force, such as its equation of state, phase diagram, and transport properties (viscosity, electrical conductivity, etc.) is an active research area today. Collisions