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modeling and numerical methods Experience with multiphase flow modeling (e.g., TFM, CFD-DEM, DNS, LBM) Solid programming skills Experience working in Linux/HPC environments Ability to conduct independent
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research potential. Candidates will be considered with background in the areas of hyperbolic PDEs and optimization problems. Expertise in numerical simulations (with Matlab or Python) will be valued
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on the development and analysis of continuous and discrete models in connection with convex and nonconvex optimization problems and monotone inclusion systems. Our ideal candidate already has experience with modern
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probability, stochastic modeling, scientific computing, mathematical optimization and control, numerical analysis, and relevant adjacent fields Appl Deadline: 2025/11/11 04:59 AM UnitedKingdomTime (posted 2025
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: Applications accepted all year round Details Data assimilation combines physical models with experimental or numerical data to produce dynamically consistent flow reconstructions. In turbulence, where full
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focus on the development and analysis of continuous and discrete models in connection with convex and nonconvex optimization problems and monotone inclusion systems. Our ideal candidate already has
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sputtering). A coupled approach, involving experiments and Multiphysics numerical simulation will be implemented. A thorough investigation of the relationships between deposition conditions (temperatures
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previous work to channel flows, both with and without wall roughness. The research will focus on, firstly, optimal synchronisation of minimal unit turbulence (MUT), leveraging the simplified dynamics in MUT
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group focus on the development and analysis of continuous and discrete models in connection with convex and nonconvex optimization problems and monotone inclusion systems. Our ideal candidate already has
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activity and stochasticity). For example, localized dendritic activation underlies numerous computational functions across hierarchical levels, such as denoising (filtering), increased expressivity (tunable