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- MOHAMMED VI POLYTECHNIC UNIVERSITY
- Oak Ridge National Laboratory
- Aalborg University
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- Argonne
- Center for Theoretical Physics PAS
- Delft University of Technology (TU Delft); yesterday published
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linear algebra computations, building software for scientific applications using GPUs (Graphics Processing Unit), multi-threading and parallelism, numerical discretization methods (finite differences
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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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. 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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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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substantial knowledge and research experience in areas such as computational fluid dynamics, turbulence modeling, data-driven methodologies, machine learning, and parallel computing. The candidate should also
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of numerical quantum many-body methods to study model Hamiltonians. Strong background in linear algebra. Preferred Qualifications: Experience with density matrix renormalization group and tensor network
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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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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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substantial knowledge and research experience in areas such as computational fluid dynamics, turbulence modeling, data-driven methodologies, machine learning, and parallel computing. The candidate should also
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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