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conjecture, -- Langlands program and related problems, -- algebraic geometry and complex geometry, -- partial differential equations and in particular Navier-Stokes equations, -- stochastic analysis
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Assistant (Postdoc) to join the research team led by Univ. Prof. Olga Mula. Our group’s work sits at the forefront of numerical analysis for Partial Differential Equations, enriched with data-driven
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of numerical analysis for Partial Differential Equations, enriched with data-driven methodologies -- a powerful combination that’s redefining what’s possible in computational science, and is playing a crucial
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related field with expertise in one or more of the following areas: Finite element methods for partial differential equations Multiscale numerical methods Flow and transport in porous media Scientific
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University of British Columbia | Northern British Columbia Fort Nelson, British Columbia | Canada | 11 days ago
for partial differential equations (PDEs). The Fellow will be responsible for designing, developing and implementing highly efficient, robust and stable numerical methods for mechanobiochemical models
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work on optimization and optimal control related to partial differential equations with emphasis on new developments related to machine learning and data science. Your profile: • Doctorate related
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propagation problems, stochastic partial differential equations, geometric numerical integration, optimization, biomathematics, biostatistics, spatial modeling, Bayesian inference, high-dimensional data, large
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; knowledge in numerical methods and simulation, particularly for partial differential equations, and basic knowledge in mathematical modeling with/and PDEs, with a focus on fluid or biomechanics, porous media
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differential equations. • Basic knowledge in mathematical modeling with/and partial differential equations, with a focus on fluid or biomechanics, porous media. • Optional/advantageous: Experience with Lattice
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computationally challenging. To address this, our research employs advanced computational methods to simplify high-fidelity 1-D hydrodynamic models based on Partial Differential Equations (PDEs). This approach