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Field
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descriptive and appropriately detailed manner that can be understood by both researchers and future Research Software Engineers. Serve as liaison with Princeton Research Computing staff on GPU cluster related
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-of-the-art foundation models and large vision-language models. Experience in large-scale deep learning systems and/or large foundation model, and the ability to train models using GPU/TPU parallelization
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foundation models and large vision-language models. Experience in large-scale deep learning systems and/or large foundation model, and the ability to train models using GPU/TPU parallelization. Experience
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benchmark them with a realistic case study. The main focus of the project can develop either more in the mathematical theory of MCMC, the implementation of code for the Jülich supercomputers (GPU/CPU
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will be considered a plus: Data analysis libraries such as Dask Knowledge and experience with Python, Fortran and/or GPU computing HPC and parallel libraries such as OpenMP and MPI HPC parallel IO
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and client-based firewalls. General hardware troubleshooting and repair skills. Experience supporting the installation, configuration, and monitoring of Nvidia GPU cards and required drivers and
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and client-based firewalls. General hardware troubleshooting and repair skills. Experience supporting the installation, configuration, and monitoring of Nvidia GPU cards and required drivers and
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troubleshooting and repair skills. Experience supporting the installation, configuration, and monitoring of Nvidia GPU cards and required drivers and software within Linux. Experience supporting researchers in
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cluster & GPUs — and deep integration with clinical data including electronic health records A diverse array of ongoing research, education, patient care, and community centered activities that require
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the following is preferred: Training and optimizing ML algorithms on GPU hardware architectures, specifically NVIDIA based Statistics, linear algebra, and data structures Exploratory data analysis and