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and have prior experience with remote GPU and HPC services. After the qualification requirements, great emphasis will be placed on personal skills. Target degree: Doctoral degree Information regarding
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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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, including experience with large-scale GPU or cluster-based training, reproducible experiment pipelines, versioned datasets, and systematic evaluation. Ability to formulate research questions, run empirical
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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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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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using clusters like UPPMAX and GPUs for high-performance computing and parallel computing using clusters like UPPMAX and GPUs for high-performance computing are essential. While not required, experience
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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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spatial datasets efficiently. Research‑grade software engineering skills (version control, testing, reproducibility, documentation); open‑source contributions are meriting. Experience with GPU/high