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Field
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duties. Ability to effectively manage time and see assigned parts of projects through to completion on deadline. Proven ability to juggle multiple related projects in parallel with concurrent and staggered
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Serve as the Lead for the team ensuring smooth operation of the Linux cluster consisting of 300+ GPU/CPU compute nodes including parallel filesystems and high-performance network. This is partly
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to explore include mono-chromatic and multi-chromatic illumination, structured illumination, and telecentric illumination distortions. Computer based image analysis approaches will be developed, including
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permeability selection in variant membrane chemistries. This work will run in parallel to experimental analysis conducted at the University of Exeter using synthetic vesicles to observe permeability
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be considered an asset Proven record in publication
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computing frameworks (e.g., MPI, NCCL) and model parallelism techniques. Proficiency in C++/CUDA programming for GPU acceleration. Experience in optimizing deep learning models for inference (e.g., using
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College provides the opportunity to apply for student research assistant support, an annual allocation of funds to support travel and research, a shared computer cluster for parallel computation, a grants
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include high-performance computing, middleware and computing systems. Recent relevant research experience to include: Undertaking research in the area of high-performance / distributed / parallel computing
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of custom computer code, and the processing of large-scale simulations using massively parallel resources such as NERSC and Raj cluster at Marquette. Conduct research with a significant degree of independence