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Field
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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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-performance computing, including parallel or GPU programming (MPI, OpenMP, CUDA, Kokkos, etc.) Familiarity with modern software development practices, including debugging, profiling, and version control
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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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or more GPUs; ability to work with pre-existing codebases and get a training run going Research interest in one or more of the following: Applied ML, Natural Language Processing, Computer Vision
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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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well as access to the group dedicated computing cluster environment with H100, L40s, and A40 GPUs. This post is funded by the UKRI Future Leaders Fellowship, a flexible long-term public funding scheme
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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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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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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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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