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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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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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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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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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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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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
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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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showcases and visualisations; and scaling and optimising data science and AI research to leverage HPC and GPU infrastructure. This post is full time and open ended (permanent) Apply online at: Research
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neural networks to harden object detection and semantic segmentation, as well as maintaining the real-time performance of the hardened AI computing on embedded GPUs (NVIDIA Jetson AGX Orin). RGB camera and
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applications for computer vision. Experience using C++ to develop and run software applications. Experience using GPU-accelerated ML and related frameworks. Experience with Docker or other containerized