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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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(parallelization, efficient data structures), numerical testing, and results analysis. Familiarity with numerical methods, scientific programming in C++, and an interest in reservoir engineering problems
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, you’ll join a technically driven, publication active team known for research in computational modelling, CFD, numerical methods and high-performance computing, with a strong culture of code quality, open
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evaluate numerical methods that combine low and high order formulations, and develop hybrid discretisation frameworks that apply varying fidelity across the computational domain. This includes creating
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description With the increasing complexity of numerical simulation codes, new
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. Experience in numerical methods and CFD development using mesh-based scientific codes. Expertise in the lattice Boltzmann method (LBM) as evidenced by their publications High performance computing (HPC
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steady and transient state, at scales ranging from nanometres to millimetres. Develop numerical methods to capture droplets evaporative behavior accurately Compare and validate numerical results with
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speech at the pinnacle of complexity. Like human babies, songbirds learn their vocalizations early in life from a social tutor. Numerous parallels to human speech learning, including analogous neural
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algorithms in the context of sparse tensor operations and apply them to real-world datasets. Parallel Computing: Explore opportunities for parallelism in the tensor completion process to enhance computational
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Fall 2026. Knowledge of parallel programming and experience developing methods for 2D and 3D problems are critical. Experience working with open source software frameworks and/or using modern open