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%). You will work on the extension of the DUNE-FEM package to support computations on GPU hardware with various types of Finite Element methods. This work is embedded in a research project considering
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to support computations on GPU hardware with various types of Finite Element methods. This work is embedded in a research project considering structure preserving Finite Element methods for multiphase flows
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Excellent programming skills in PyTorch/JAX and experience working with GPUs and high-performance clusters. Strong mathematical skills with excellent understanding of statistics (e.g., hypothesis testing
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scenarios Convolutional neural networks for computer vision require substantial computing resources and introduce significant latencies even in modern GPU systems. This project investigates neuromorphic
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models, LLMs and Transformer architectures Excellent programming skills in PyTorch/JAX and experience working with GPUs and high-performance clusters. Strong mathematical skills with excellent
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learning, multicore and GPU programming, and highly parallel systems. Good knowledge in one or more of the following programming languages/environments: C/C++, Python, PyTorch (or similar), and Cuda. Place
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precision algorithms for CPUs and GPUs. Performance engineering and analysis including application profiling, benchmarking to identify performance bottlenecks. Verification, and validation of the developed
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with GPU/high‑performance computing or scaling methods to large‑scale inference. What you will do Take courses at an advanced level within the Graduate school of Mathematics or Applied Mathematics and
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exposing hardware accelerators, such as GPUs and FPGAs, in a seamless and portable way. This includes designing execution logic and resource-scheduling strategies that make efficient use of available
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tasks across distributed infrastructures. A key aspect of the position involves integrating and exposing hardware accelerators, such as GPUs and FPGAs, in a seamless and portable way. This includes