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, which has multiple test machines with GPUs and AI accelerators. The algorithms used can be bound by the available compute power or memory bandwidth in different parts of the program. This information will
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provide a performance or efficiency advantage, and determine scenarios where conventional AI accelerators (such as embedded GPUs or FPGA-based accelerators) remain more appropriate due to data
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++ and Python programming languages. Experience in open source projects, GPU programming, distributed computing and cloud computing are considered to be strong assets. The position of Research Fellow at
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optimisation, distributed-parallel-GPU optimisation (e.g. pagmo2), Taylor-based numerical integration of ODEs (e.g. heyoka), differential algebra and high order automated differentiation (audi), quantum