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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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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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that combine parallel architectures (i.e., GPUs or accelerator boards, clusters) and numerical algorithms suited to such architectures with the goal of improving the speed of convergence and the stability
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pipelines, and rigorously quantify reductions in energy per solve compared with optimized CPU/GPU and FPGA baselines. The project targets three real THz-NDE use cases: (i) sparse deconvolution of THz impulse
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Our org owns Meta's hardware tech strategy for AI - finding innovative hardware for GPUs and Meta's custom AI chips, as well as CPU, memory, and storage as well as getting these to work in Meta's
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or TensorFlow. Practical background in training and validating models on GPU-based and distributed computing environments. Working knowledge of containerization tools and orchestration platforms (e.g. Docker
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The University of Birmingham’s Advanced Research Computing (ARC) team is expanding following a major UKRI award to deliver the Baskerville National Compute Resource (NCR) GPU‑accelerated system. We are appointing
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datasets and run experiments on HPC infrastructure (GPU clusters, SLURM). Strong written communication skills for technical documentation, reporting, and research outputs. Ability to work independently and
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research programme Access to secure clinical and multi-omics data environments Modern GPU, and high-performance computing resources, plus dedicated research-engineering support Close integration with
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-node GPU training and inference pipelines for foundational models. You'll also develop tools for ingesting, transforming, and integrating large, heterogeneous microscopy image datasets—including writing