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- MOHAMMED VI POLYTECHNIC UNIVERSITY
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
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- Central China Normal University
- Delft University of Technology (TU Delft); yesterday published
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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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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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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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completion) in applied mathematics, computer science, or a closely related field. Strong background in numerical linear algebra, algorithm design, and parallel computing. Proficiency in programming languages
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-scale foundation models, especially pipeline/model parallelism. Track record of creating HPC software for numerical methods. Domain expertise in areas like computational fluid dynamics, material
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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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Computational Fluid Dynamics. Operational skills : Physical analysis of fluid dynamics, advanced skills in programming and numerical methods, writing scientific reports and articles, presenting at scientific
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of interpretability methods to ensure ML outputs are meaningful in scientific contexts. Preferred: Background in biomedical data, healthcare, or AI for life sciences. Experience with parallel computing. Familiarity