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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 1 month ago
) Application Deadline 22 Mar 2026 - 00:00 (UTC) Country Australia Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is
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learning and signal processing libraries; You have HPC/GPU computing experience, including running deep learning workloads on compute clusters (CUDA-compatible GPUs, multi-GPU training, Slurm). Your master's
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computing software libraries (e.g., Trilinos, MFEM, PETSc, MOOSE). Experience with shared and distributed memory parallel programming models such as OpenMP and MPI. Experience with one more GPU or performance
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NAISS, the National Academic Infrastructure for Supercomputing in Sweden, provides academic users with high-performance computing resources, storage capacity, and data services. NAISS is hosted by
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-mode taxonomies). Implement and maintain high-quality research codebases (PyTorch/HF), experiment tracking, and compute workflows (multi-GPU, HPC/cluster), ensuring reproducibility and documentation
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machine learning methods for computational materials physics and chemistry. Projects include: The aim is to develop generalized equivariant neural network models NequIP and Allegro for machine learned
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-time Postdoctoral Researcher to join our team in building next-generation Foundation Models for Earth System Modeling at NYU Courant Institute School of Mathematics, Computing, and Data Science. In
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full-time Postdoctoral Researcher to join our team at NYU Courant Institute School of Mathematics, Computing, and Data Science. In collaboration with Profs Carlos Fernandez-Granda, Joan Bruna, Laure
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for Neural Rendering for Computer Graphics and Real-Time Rendering. By using ANNs, coded for high-performance on cross-vendor GPUs, we aim to create new techniques for global illumination and material models
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We are seeking a highly motivated PhD student to perform fundamental research and to conceive truly sparse solutions (on both, CPU and GPU) for dynamic sparse training, aiming to cut the training