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mathematics and engineering. The Interpretable Machine Learning Lab has dedicated access to high-performance CPU and GPU computing resources provided by Duke University’s Research Computing unit and state
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Dr Edward Johns, as well as a larger team across the UK as part of the ARIA-funded Robot Dexterity programme (see: https://www.aria.org.uk/opportunity-spaces/smarter-robot-bodies/robot-dexterity
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techniques. Preferred Qualifications: Knowledge of HPC matrix, tensor and graph algorithms. Knowledge of GPU CUDA and HIP programming Knowledge on distributed algorithms using MPI and other frameworks such as
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Mathematica and Python with an interest in GPU programming. These required and desired skills should be demonstrated by presenting an existing body of code and/or peer-reviewed publications. Additional
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. First, efficient and scalable training procedure are still needed, irrespective of whether the training is done off-line on a traditional GPU-based architecture, on neuromorphic hardware. Second
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libraries for modern architectures (e.g., GPUs). Exploration of linear algebra methods in computational physics applications and machine learning. Integrate and benchmark the GINGKO library, a sparse solver
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for modern heterogeneous architectures, including CPUs, GPUs, and other accelerators, the project seeks to achieve unprecedented efficiency and resolution in plasma simulations. This advancement will enable
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, Probabilistic Inference, Algebraic Topology and Wavelet analysis theory. Familiar with Matlab/Python/C++ programming. Experience with Pytorch and multi-GPU model deployment. Experience in analyzing complex
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, Probabilistic Inference, Algebraic Topology and Wavelet analysis theory. Familiar with Matlab/Python/C++ programming. Experience with Pytorch and multi-GPU model deployment. Experience in analyzing complex
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turbulence. Experience with GPU programming, FPGA, and DNN in image recognition is a great plus. Track record of publications and conference presentations. Experience with hands on lab work. FLSA Exempt Full