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optimization layers Increase inference efficiency (e.g., GPU acceleration) and assess the applicability domain of learned algorithms Publish and present your results in peer-reviewed journals and at
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, Pandas, SQL, Docker, git, etc. PyTorch skills: experience in training machine learning models with one or more GPUs; ability to work with pre-existing codebases and get a training run going A versatile
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the necessary algorithms. You will also develop and document a graphical user interface which handles large processing tasks efficiently and uses multiprocessing and GPU acceleration where necessary. At the same
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models on one or more GPUs and the ability to work with existing codebases to set up training runs Research interest in one or more of the following areas: probabilistic machine learning, time series
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- and multi-GPU setups, and ability to work with existing codebases to quickly get training pipelines running Deep research interest in one or more of the following areas: 3D Gaussian Splatting, Neural
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plasma physics (XGC, IPPL). Expected qualifications: A Master's degree in Computer Science or Applied Mathematics. Necessary knowledge: Modern C++, GPU computing with CUDA/SYCL, MPI, Krylov solvers
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on high performance computing systems or cloud infrastructure (including GPU-accelerated workloads). Practical experience with modern deep learning frameworks, model serving in production, and building end
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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hydrodynamics and/or N-body simulations in the star and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be
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for prototyping. Interest and affinity for high-performance computing are necessary for the position. You should have experience with the roofline model and familiarity with a profiler . Experience with GPUs is a