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group and develop an innovative and impactful research program, which includes, but is not limited to, population and evolutionary genetics, metagenomics, eDNA-based monitoring, and phylogenomics
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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: SIMD performance engineering. Machine Learning. Communication-efficient
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implemented in the Fortran programming language, and it relies on the platform CUDA for parallelization of the computation over several GPUs’ cores, and has interfaces with Matlab and Python for ease of use
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of code to utilize GPU-acceleration on DTU’s high-performance computing cluster or other HPC systems. You will also analyze realistic physical implementations of the architectures you explore, with a
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on conventional computing platforms such as GPUs, CPUs and TPUs. As language models become essential tools in society, there is a critical need to optimize their inference for edge and embedded systems
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group and develop an innovative and impactful research program, which includes, but is not limited to, population and evolutionary genetics, metagenomics, eDNA-based monitoring, and phylogenomics