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integration, finite-volume and finite-element methods, variational formulations, structure-preserving discretisations, optimal transport, and the numerical analysis of partial differential equations. The second
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, structure-preserving discretisations, optimal transport, and the numerical analysis of partial differential equations. The second position will focus primarily on the geometric representation of complex data
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applications. This includes, but is not limited to, stochastic differential equations, stochastic partial differential equations, variational and geometric methods, probabilistic numeric, optimal transport, and
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of neural networks, information-theoretic and optimal-transport perspectives on representation and generalisation, probabilistic numerics and Bayesian deep learning, and emerging frameworks for scientific
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experimental PhDs and Canon Production Printing to enable data-driven ink optimization. Why Join? Work in the multidisciplinary Processing and Performance of Materials group Work at the forefront of sustainable