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Processing Unit (GPU) hardware. Working Conditions Needs to be able to successfully perform all required duties. Office/research environment; some travel and weekend work is required. UTRGV is a distributed
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programming languages. Experience with DICOM data, medical-image registration, high-performance computing, or GPU-based computation. Familiarity with machine-learning or deep-learning methods for medical-image
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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computational modelling of additive manufacturing and develop high-performance GPU-based CFD solvers. Qualifications • With PhD degree • Strong research experience in developing GPU-based
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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parallel programming and/or high-performance computing, particularly on GPU or FPGA architectures; Knowledge of compression techniques, including predictive coding, filter banks, transforms, and statistical
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the speed up from using GPUs as well as machine learning techniques, e.g. simulation-based inference. Finally, we will use similar techniques to make a statistical inference of the population of subhaloes by
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optimization Familiarity with continuous-variable/bosonic systems, non-Gaussian states, multimode entanglement, or Wigner-function methods Experience with HPC workflows (cluster computing, GPU computing
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streams with perturbation signatures and fit these. For these fits, we will explore the speed up from using GPUs as well as machine learning techniques, e.g. simulation-based inference. Finally, we will use
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hardware (e.g., GPUs and/or Non-volatile memory) and data science applications.