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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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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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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
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the Numerical Analysis group at TU Delft. The group is internationally recognized for its contributions to iterative methods, numerical linear algebra, and parallel computing. The project will be carried out in
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hired at BTH will mainly focus on two aspects of neuromorphic computing: Guidelines / frameworks for mapping applications to neuromorphic systems. Efficient training methods of neuromorphic applications
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Inria, the French national research institute for the digital sciences | Toulouse, Midi Pyrenees | France | 18 days ago
an M2 degree (Engineering degree, MRes, or equivalent) in applied mathematics or computational mechanics. The candidate should have experience with numerical methods for PDEs, and should have good