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Position Details Position Information Recruitment/Posting Title Lead Software Developer – GPU-accelerated Free Energy Simulation and Machine Learning Methods Department Quantitative Biomedicine Inst
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, automating, and optimizing the GPU and AI and computing infrastructure used by researchers across the university, along with the high-performance computing systems that support it. This position will be filled
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, automating, and optimizing the GPU and AI and computing infrastructure used by researchers across the university, along with the high-performance computing systems that support it. The position will be filled
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development and GPU-accelerated inference to change what can be attempted by a small research group. The postholder will derive physical predictions, direct the construction of the software needed to test them
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extracting transport properties from molecular dynamics trajectories. ● Experience with GPU-accelerated machine learning frameworks (for example CUDA, PyTorch, or GPU-enabled LAMMPS). ● Experience
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turbulent flow simulations. 3. Background in numerical methods and scientific computing. 4. Proficiency in Python programming and high-performance computing on modern GPU-based platforms
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scalable training and inference on parallel, distributed and GPU-accelerated computing systems Benchmark the developed approaches against established methods, assessing predictive performance, generalisation
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GPUs (B200s and cloud credits) and GPUs (A100s and H200s) The opportunity to continue your career at a world-leading institution Sector-leading salary and remuneration package (including 43 days off a
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capabilities. Technical Skills - Desired Experience with Cray EX supercomputers with NVIDIA GPUs. Experience with Kubeflow pipelines and Kubeflow Training Operator. Experience with distributed inference
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towards a sustainable future. Subject description This project focuses on the development of GPU-accelerated, high-fidelity thermal runaway simulation models for lithium-ion battery cells, modules, packs