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Field
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Engineering, or a related field Strong experience in building and optimizing AI systems using PyTorch, TensorFlow, or JAX Practical knowledge of NVIDIA GPU programming (CUDA) and experience with inference
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transcriptomics. Innovative visualization tools and highly automated analytical pipelines powered by GPU technology. Mentorship from experienced scientists in data analysis and management, with an expertise in
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through HPC - Monitor and stay current with trends in research computing, such as container technology, latest gpu and cpu hardware, hpc cluster management tools, storage tools/administration and cluster
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Selectris energy filter, Vitrobot Mk. IV, GPU- and data storage-cluster and ready access to Titan Krioses at the Francis Crick Institute, LonCEN consortium, and the UK national cryoEM facility at eBIC. What
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systems and GPU-accelerated processing. Proficient in computing languages for software/hardware integration, data analysis, networking; specifically, Python and C/C++. Proficient with Linux computer systems
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4000 Ada GPUs and over 30,000 CPU cores) hosted at the project data center in Nevada where the telescope is located. Automation: Help in developing and implementing automated processes for server
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. Optimizes the performance and scalability of AI/ML workloads through algorithmic and system-level improvements, including evaluation and tuning of CPU vs. GPU usage for cost-effectiveness. Monitors and
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free text of both biomedical literature and electronic patient records exploiting HPC, including GPUs embedded within NHS infrastructure. Development and deployment of ML operations software and tooling
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and train CNN and SNN models utilizing frameworks such as Keras, PyTorch, and SNNtorch Implement GPU acceleration through CUDA to enable efficient neural network training Apply hardware-aware design
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an early member of a friendly, multi-disciplinary team. The project will involve significant coding using the Julia package Molly.jl on both CPU and GPU, as well as addressing relevant computational and