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algorithms Improve performance through tuning the GPU code Document, test, and debug revisions of the software for release Coordinate conversion of LM states of cells into coarse-grained atomistic
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services, which provide expert support in data management and high-performance computing, including optimized pipelines and large-scale GPU resources. A competitive salary and benefits package, with
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, evidenced by e.g. projects on Github. Familiarity with deep learning hardware / accelerators and GPU/CUDA kernels.
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server equipped with last generation GPUs. • Structural characterization by X-ray protein crystallography and/or cryo-EM. • Functional and biophysical characterization in vitro of enzymatic activities
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and aerial data. Analysis of large wildlife databases: neural networks. Computing clusters with CPU/GPU. Specific Requirements Educational Requirememts: Machine learning. Signal processing. Signal
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performance computer architectures, specifically GPUs. Responsibilities /Tasks Develop three-dimensional 6-moment gyro-fluid simulation code in particular to include plasma-neutral interactions, multispecies
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, which provide expert support in data management and high-performance computing, including optimized pipelines and large-scale GPU resources. A competitive salary and benefits package, with relocation
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to leverage CPU and GPU cluster computing resources for large-scale image analysis. Train and mentor users on a variety of microscopy modalities, including confocal, STED, SIM, FLIM, TIRF, STORM, HCA, and
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the U.S. (https://www.rc.ufl.edu/about/hipergator/ ), and the AI NVIDIA GPU SuperPOD (https://news.ufl.edu/2020/07/nvidia-partnership/ ) supporting UF’s campus-wide AI initiative (https://ai.ufl.edu
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://www.rc.ufl.edu/about/hipergator/ ), and the AI NVIDIA GPU SuperPOD (https://news.ufl.edu/2020/07/nvidia-partnership/ ) supporting UF’s campus-wide AI initiative (https://ai.ufl.edu ). These resources are available