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Work location Zürich or Lugano Topic B: Simultaneous tree traversal with Producer-Consumer pattern on GPUs Abstract Simultaneous tree traversal, also referred to as dual tree traversal, can be applied
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foundations. Candidates should possess an exceptional academic record and a strong mathematical background. Experience conducting large-scale computational experiments (e.g., multi-GPU systems) is advantageous
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of computer graphics fundamentals, numerical methods, and GPU/parallel computing concepts. Experience with at least one major deep learning framework (PyTorch preferred). Excellent problem-solving skills and
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optimization – with rigorous theoretical analysis. The ideal candidate has strong machine learning and AI expertise and is comfortable with – or eager to learn – large-scale multi-GPU experimentation
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megawatts. To transfer energy efficiently from the grid to CPUs/GPUs, higher system voltages are required in data centres/computer racks, and efficient power electronics converter systems based on SSTs
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applications. HPC and orchestration of scientific data processing workflows. Parallel computing (GPU & CPU). good software engineering practices for scientific software (version control, testing, continuous
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, including running large-scale machine learning models (e.g., PyTorch, JAX) on GPUs in an HPC environment and maintaining reproducible workflows. Extensive prior experience developing pipelines and analytic
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responsibility for our unique GPU-accelerated 3D FDTD software suite and extending its capabilities Modelling the effects of atmospheric turbulence fields Software development (3D modelling and coding in Python, C
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signal processing and/or survey datasets. ML & AI techniques and applications. HPC and orchestration of scientific data processing workflows. Parallel computing (GPU & CPU). good software engineering
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the Alps supercomputer at the Swiss National Supercomputing Centre (CSCS), which features over 10,000 NVIDIA Grace Hopper GPUs, making it one of the most powerful AI-focused computing resources in